#Required packages
if(!require(readxl)){install.packages("readxl"); library(readxl)}
## Loading required package: readxl
if(!require(correlation)){install.packages("correlation"); library(correlation)}
## Loading required package: correlation
if(!require(corrplot)){install.packages("corrplot"); library(corrplot)}
## Loading required package: corrplot
## corrplot 0.92 loaded
if(!require(car)){install.packages("car"); library(car)}
## Loading required package: car
## Loading required package: carData
if(!require(mgcv)){install.packages("mgcv"); library(mgcv)}
## Loading required package: mgcv
## Loading required package: nlme
## This is mgcv 1.9-0. For overview type 'help("mgcv-package")'.
if(!require(rpart)){install.packages("rpart"); library(rpart)}
## Loading required package: rpart
if(!require(ggplot2)){install.packages("ggplot2"); library(ggplot2)}
## Loading required package: ggplot2
if(!require(gridExtra)){install.packages("gridExtra"); library(gridExtra)}
## Loading required package: gridExtra
if(!require(lme4)){install.packages("lme4"); library(lme4)}
## Loading required package: lme4
## Loading required package: Matrix
## 
## Attaching package: 'lme4'
## The following object is masked from 'package:nlme':
## 
##     lmList
if(!require(Matrix)){install.packages("Matrix"); library(Matrix)}
if(!require(lmtest)){install.packages("lmtest"); library(lmtest)}
## Loading required package: lmtest
## Loading required package: zoo
## 
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric
if(!require(gamm4)){install.packages("gamm4"); library(gamm4)}
## Loading required package: gamm4
## This is gamm4 0.2-6
if(!require(sjPlot)){install.packages("sjPlot"); library(sjPlot)}
## Loading required package: sjPlot
if(!require(sjmisc)){install.packages("sjmisc"); library(sjmisc)}
## Loading required package: sjmisc
## Learn more about sjmisc with 'browseVignettes("sjmisc")'.
if(!require(sjlabelled)){install.packages("sjlabelled"); library(sjlabelled)}
## Loading required package: sjlabelled
## 
## Attaching package: 'sjlabelled'
## The following object is masked from 'package:ggplot2':
## 
##     as_label
if(!require(performance)){install.packages("performance"); library(performance)}
## Loading required package: performance
if(!require(flexmix)){install.packages("flexmix"); library(flexmix)}
## Loading required package: flexmix
## Loading required package: lattice
if(!require(glmmTMB)){install.packages("glmmTMB"); library(glmmTMB)}
## Loading required package: glmmTMB
## Warning in checkDepPackageVersion(dep_pkg = "TMB"): Package version inconsistency detected.
## glmmTMB was built with TMB version 1.9.10
## Current TMB version is 1.9.11
## Please re-install glmmTMB from source or restore original 'TMB' package (see '?reinstalling' for more information)
## 
## Attaching package: 'glmmTMB'
## The following object is masked from 'package:flexmix':
## 
##     refit
if(!require(DHARMa)){install.packages("DHARMa"); library(DHARMa)}
## Loading required package: DHARMa
## This is DHARMa 0.4.6. For overview type '?DHARMa'. For recent changes, type news(package = 'DHARMa')

Import Dataset

#Import data
week_kuds <- read.csv("week_kuds2.2.csv", sep=";") #has the wrong week values
week_kuds3 <- read.csv("week_kuds1 - usar.csv", sep=";") #has the right week values
week_kuds$Week <- week_kuds3$Week #substitute the wrong for the right week in the dataset
#create week variable without the year
names(week_kuds)[names(week_kuds) == "Week"] <- "WeekYear"
week_kuds$Week <- substr(week_kuds$WeekYear, 1, 2)
#create year variable without the week
week_kuds$Year <- substr(week_kuds$WeekYear, 4, 7)

week_kuds$File <- as.factor(week_kuds$File)
week_kuds$Species <- as.factor(week_kuds$Species)
week_kuds$Transmitter <- as.factor(week_kuds$Transmitter)
week_kuds$KUD50 <- as.numeric(week_kuds$KUD50)
week_kuds$KUD95 <- as.numeric(week_kuds$KUD95)
week_kuds$Habitat <- as.factor(week_kuds$Habitat)
week_kuds$Migration <- as.factor(week_kuds$Migration)
week_kuds$ComImport <- as.factor(week_kuds$ComImport)
week_kuds$Length_cm <- as.numeric(week_kuds$Length_cm)
week_kuds$LengthStd <- as.numeric(week_kuds$LengthStd)
week_kuds$BodyMass <- as.numeric(week_kuds$BodyMass)
week_kuds$BodyMassStd <- as.numeric(week_kuds$BodyMassStd)
week_kuds$Longevity <- as.numeric(week_kuds$Longevity)
week_kuds$Vulnerability <- as.numeric(week_kuds$Vulnerability)
week_kuds$Troph <- as.numeric(week_kuds$Troph)
week_kuds$ReceiverDensity <- as.numeric(week_kuds$ReceiverDensity)
week_kuds$MonitArea_km2 <- as.numeric(week_kuds$MonitArea_km2)
week_kuds$MCP_km2 <- as.numeric(week_kuds$MCP_km2)
week_kuds$NReceivers <- as.numeric(week_kuds$NReceivers)
week_kuds$MaxDistReceivers <- as.numeric(week_kuds$MaxDistReceivers)
week_kuds$MaxLength <- as.numeric(week_kuds$MaxLength)
week_kuds$MaxBodyMass <- as.numeric(week_kuds$MaxBodyMass)
week_kuds$a <- as.numeric(week_kuds$a)
week_kuds$b <- as.numeric(week_kuds$b)
week_kuds$Week <- as.factor(week_kuds$Week)
week_kuds$Year <- as.factor(week_kuds$Year)
week_kuds$Spawn <- as.factor(week_kuds$Spawn)
summary(week_kuds)
##                       File          Species            Transmitter   
##  Seriola_rivoliana      : 2787   Dsar   :3708   A69-1008-104 :  284  
##  Epinephelus_marginatus1: 2055   Gmor   :3170   A69-1008-102 :  281  
##  Gadus_morhua1          : 1635   Emar   :3012   A69-1008-103 :  264  
##  Pseudocaranx_dentex    : 1527   Sriv   :2787   A69-1008-100 :  261  
##  Diplodus_sargus4       : 1474   Svir   :1856   A69-1303-6685:  252  
##  Sphyraena_viridensis1  : 1298   Pden   :1527   A69-1008-101 :  247  
##  (Other)                :14836   (Other):9552   (Other)      :24023  
##      KUD50            KUD95          WeekYear                    Habitat     
##  Min.   :0.1620   Min.   : 0.760   Length:25612       benthopelagic  : 9100  
##  1st Qu.:0.1640   1st Qu.: 0.762   Class :character   demersal       :11326  
##  Median :0.1730   Median : 0.818   Mode  :character   pelagic-neritic: 5186  
##  Mean   :0.2267   Mean   : 1.124                                             
##  3rd Qu.:0.2390   3rd Qu.: 1.188                                             
##  Max.   :3.2780   Max.   :14.723                                             
##                                                                              
##          Migration      ComImport       Length_cm        LengthStd     
##  non-migratory:14095   high  :15837   Min.   : 12.20   Min.   :0.1375  
##  oceanodromous:11517   medium: 9572   1st Qu.: 33.00   1st Qu.:0.3474  
##                        minor :  203   Median : 46.50   Median :0.4926  
##                                       Mean   : 49.95   Mean   :0.4853  
##                                       3rd Qu.: 62.00   3rd Qu.:0.6000  
##                                       Max.   :118.00   Max.   :1.0200  
##                                                                        
##     BodyMass        BodyMassStd         Longevity     Vulnerability  
##  Min.   :   19.7   Min.   :0.001964   Min.   : 8.00   Min.   :35.90  
##  1st Qu.:  523.1   1st Qu.:0.049854   1st Qu.:13.50   1st Qu.:63.40  
##  Median : 1134.0   Median :0.109527   Median :22.70   Median :66.30  
##  Mean   : 2436.8   Mean   :0.172517   Mean   :24.81   Mean   :64.47  
##  3rd Qu.: 2829.9   3rd Qu.:0.228268   3rd Qu.:29.00   3rd Qu.:71.50  
##  Max.   :22826.3   Max.   :1.490344   Max.   :60.00   Max.   :90.00  
##                                                                      
##      Troph       ReceiverDensity   MonitArea_km2       MCP_km2         
##  Min.   :2.860   Min.   :  0.030   Min.   : 0.690   Min.   :   0.1447  
##  1st Qu.:3.470   1st Qu.:  0.130   1st Qu.: 1.500   1st Qu.:   2.0148  
##  Median :4.090   Median :  0.330   Median : 7.640   Median :  12.2923  
##  Mean   :3.935   Mean   :  8.943   Mean   : 6.752   Mean   : 259.2622  
##  3rd Qu.:4.310   3rd Qu.: 12.410   3rd Qu.: 8.380   3rd Qu.: 355.9882  
##  Max.   :4.660   Max.   :131.310   Max.   :15.620   Max.   :2990.2885  
##                                                                        
##    NReceivers    MaxDistReceivers   MaxLength      MaxBodyMass   
##  Min.   : 4.00   Min.   : 0.53    Min.   : 38.0   Min.   :  585  
##  1st Qu.:19.00   1st Qu.: 3.21    1st Qu.: 65.9   1st Qu.: 3000  
##  Median :45.00   Median : 9.25    Median :122.0   Median :14300  
##  Mean   :43.15   Mean   :34.78    Mean   :116.1   Mean   :31655  
##  3rd Qu.:55.00   3rd Qu.:74.03    3rd Qu.:150.0   3rd Qu.:60000  
##  Max.   :98.00   Max.   :87.39    Max.   :200.0   Max.   :96000  
##                                                                  
##        a                 b         SpawnSeason              Tp        
##  Min.   :0.00391   Min.   :2.801   Length:25612       Min.   :   2.0  
##  1st Qu.:0.00900   1st Qu.:2.880   Class :character   1st Qu.: 292.0  
##  Median :0.01010   Median :3.060   Mode  :character   Median : 418.0  
##  Mean   :0.01437   Mean   :3.021                      Mean   : 731.2  
##  3rd Qu.:0.01933   3rd Qu.:3.120                      3rd Qu.:1465.0  
##  Max.   :0.09550   Max.   :3.280                      Max.   :2413.0  
##                                                                       
##        RI             ROM_mh            Week            Year      Spawn     
##  Min.   :0.0000   Min.   :  0.00   38     :  572   2008   :4050   A : 1335  
##  1st Qu.:0.6300   1st Qu.:  8.40   37     :  568   2011   :3583   SS:22642  
##  Median :0.8100   Median : 21.20   39     :  556   2021   :3128   W : 1635  
##  Mean   :0.7518   Mean   : 38.03   35     :  548   2009   :2073             
##  3rd Qu.:0.9400   3rd Qu.: 45.40   30     :  546   2007   :1906             
##  Max.   :1.0000   Max.   :708.30   24     :  545   2010   :1501             
##                                    (Other):22277   (Other):9371
#Number of individuals by species
num_transmissores <- aggregate(Transmitter ~ Species, data = week_kuds, FUN = function(x) length(unique(x)))

barplot(num_transmissores$Transmitter, 
        names.arg = num_transmissores$Species, 
        xlab = "Species", 
        ylab = "Number of Individuals",
        col = "deepskyblue", 
        border = "black",
        ylim= c(0,180),
        yaxp = c(0, 180, 3),
        cex.names = 0.8,
        las = 2)
text(x = barplot(num_transmissores$Transmitter, plot = FALSE), 
     y = num_transmissores$Transmitter, 
     label = num_transmissores$Transmitter, 
     pos = 3, 
     cex = 0.7)

#Number of observations by species
contagem_observacoes <- table( week_kuds$File, week_kuds$Species)

barplot(contagem_observacoes, 
        xlab = "Species", 
        ylab = "Number of Observations", 
        col= c("deepskyblue"),
        border = "black",
        ylim = c(0, 4000),
        yaxp = c(0, 4000, 8),
        cex.names = 0.8,
        las = 2)

ggplot(week_kuds, aes(LengthStd, KUD95, color=File)) +
         geom_point() +
  labs(title = "KUD95 ~ Length por File")

ggplot(week_kuds, aes(BodyMassStd, KUD95, color=File)) +
         geom_point() +
  labs(title = "KUD95 ~ BodyMass por File")

ggplot(week_kuds, aes(Vulnerability, KUD95, color=File)) +
         geom_point() +
  labs(title = "KUD95 ~ Vulnerability por File")

ggplot(week_kuds, aes(Longevity, KUD95, color=File)) +
         geom_point() +
  labs(title = "KUD95 ~ Longevity por File")

ggplot(week_kuds, aes(Troph, KUD95, color=File)) +
         geom_point() +
  labs(title = "KUD95 ~ Troph por File")

ggplot(week_kuds, aes(LengthStd, KUD95)) +
         geom_point() +
         facet_wrap(~File, ncol=7) +
  labs(title = "KUD95 ~ Length por File")

Linear Model Assumptions

Normality Assumption

KUD95

summary(week_kuds$KUD95)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   0.760   0.762   0.818   1.124   1.188  14.723
hist(week_kuds$KUD95, col= "deepskyblue", main = "KUD95 Frequency", xlab="KUD95", breaks = 30)

qqnorm(week_kuds$KUD95)
qqline(week_kuds$KUD95, col = 2)

boxplot(week_kuds$KUD95, col="deepskyblue", ylab = "KUD95", main = "Boxplot of KUD95")

KUD50

summary(week_kuds$KUD50)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.1620  0.1640  0.1730  0.2267  0.2390  3.2780
hist(week_kuds$KUD50, col= "green2", main = "KUD50 Frequency", xlab="KUD50", breaks = 30)

qqnorm(week_kuds$KUD50)
qqline(week_kuds$KUD50, col = 2)

boxplot(week_kuds$KUD50, col= "green2", ylab = "KUD50", main = "Boxplot of KUD50")

First of all, we saw the summary of KUD95 and KUD50, to have a better perception of the dimension of these variables. After, we investigated if this variables were Normal distributed. For that we used two methods: Histogram and Q-Q plot. If a variable is Normal distributed, it is expected to see the shape of a bell in the histogram, symmetrical around an average. In addition, in the Q-Q plot, it’s expected to see the points distributed along the reference line, without major deviations. With this said, we could see that the histograms don’t have the shape of a bell, instead they only have one side of the bell, so the points are not distributed symmetrically around an average, and the points from the Q-Q plot are very distant from the reference line. This indicate that our response variables are not Normal distributed. We also made a boxplot and as it’s possible to identify a bunch of outliers, it is further proof that the response variable doesn’t follow a Normal distribuition, otherwise the points would be more contained inside the box. The Normality assumption is not met for both response variables. The data violates the Normality Assumption.

Outliers Analysis

#First removal
boxplot.stats(week_kuds$KUD95) #superior limit where outliers start is 1.827
## $stats
## [1] 0.760 0.762 0.818 1.188 1.827
## 
## $n
## [1] 25612
## 
## $conf
## [1] 0.8137942 0.8222058
## 
## $out
##    [1]  3.494  2.506  1.990  1.943  1.911  1.923  1.906  1.931  1.969  1.884
##   [11]  1.871  1.851  1.842  1.923  1.977  2.198  2.102  2.254  2.059  1.993
##   [21]  1.862  1.833  1.914  1.840  1.881  1.950  1.963  2.062  2.061  1.945
##   [31]  2.114  1.912  1.959  1.854  1.887  1.829  2.110  2.254  1.921  2.071
##   [41]  2.030  1.990  2.043  1.996  2.074  2.948  2.175  1.843  2.186  1.847
##   [51]  1.833  1.917  1.830  1.848  1.880  1.862  1.834  1.940  2.013  1.880
##   [61]  2.046  2.011  1.890  1.996  2.650  1.915  1.911  2.112  1.959  2.016
##   [71]  1.840  2.583  2.018  2.291  2.184  2.684  2.598  1.863  1.850  2.007
##   [81]  1.863  1.962  1.859  1.988  2.412  3.857  2.186  2.458  2.594  4.133
##   [91]  3.133  2.087  2.145  1.829  2.255  2.222  2.122  2.226  2.227  2.248
##  [101]  2.205  2.066  2.026  1.881  1.993  2.109  8.410  7.846  8.805  4.991
##  [111]  3.834  6.338  8.343  6.595  7.410  7.560  6.803  7.530  5.109  5.559
##  [121]  5.943  1.882  1.896  2.081  1.995  2.254  1.996  2.209  2.101  2.215
##  [131]  2.192  2.050  1.932  2.043  2.179  1.910  2.239  1.964  2.132  2.088
##  [141]  1.994  1.894  2.307  1.906  1.925  7.752  7.273  5.443  2.245  2.754
##  [151]  2.823  3.655  7.074  7.297  2.410  2.241  1.845  2.282  2.091  2.135
##  [161]  1.883  1.844  1.959  2.447  1.864  2.127  2.095  2.113  1.848  1.857
##  [171]  2.114  2.345  2.211  2.835  3.964  4.869  2.149  2.740  1.866  2.017
##  [181]  1.926  1.989  1.929  1.932  2.196  2.081  1.961  1.947  1.828  2.818
##  [191]  2.257  2.018  2.932  3.674  2.695  3.017  2.218  2.412  3.188  3.432
##  [201]  3.214  3.654  2.391  2.068  2.110  3.779  2.213  2.763  1.842  1.928
##  [211]  2.254  3.663  3.694  3.016  2.175  3.733  2.219  2.174  2.984  2.957
##  [221]  1.926  3.514  2.182  2.023  3.849  3.615  3.768  2.181  2.203  3.541
##  [231]  3.713  3.418  2.157  2.634  2.261  2.330  1.978  2.478  2.204  3.226
##  [241]  2.163  2.707  2.753  2.795  1.892  1.841  2.052  3.841  2.171  2.124
##  [251]  2.346  1.919  2.019  1.877  2.716  4.662  1.844  2.007  1.950  2.564
##  [261]  2.128  1.855  2.799  4.898  2.081  2.024  1.862  2.253  2.233  5.602
##  [271]  1.994  1.996  1.949  3.564  3.282  3.306  2.570  2.866  3.495  3.759
##  [281]  3.517  3.326  2.138  2.861  3.365  4.136  2.711  4.482  2.947  2.276
##  [291]  3.196  1.902  4.300  1.976  2.350  2.811  2.580  2.143  2.310  2.124
##  [301]  3.354  2.123  1.903  2.123  2.701  2.731  2.893  5.206  2.251  4.528
##  [311]  3.570  2.272  2.751  2.619  4.502  2.775  4.483  5.689  2.514  7.286
##  [321]  2.030  2.073  2.116  3.374  2.435  3.440  4.270  4.847  3.389  2.024
##  [331]  3.062  2.459  2.213  3.317  1.918  4.377  6.218  1.832  3.935  3.502
##  [341]  4.627  2.368  4.496  4.090  4.301  2.478  2.395  2.035  2.095  1.839
##  [351]  2.505  2.816  4.097  2.090  1.905  1.850  2.109  2.213  3.372  3.291
##  [361]  3.445  3.413  2.982  1.836  1.906  1.931  2.053  1.857  1.981  2.216
##  [371]  2.483  6.556  3.120  2.911  6.831  7.292  1.931  3.749  2.091  2.894
##  [381]  4.314  3.429  2.991  2.512  3.412  2.822  6.282  2.099  2.991  1.982
##  [391]  1.894  1.848  3.602  1.951  2.512  1.926  3.774  3.666  3.127  2.247
##  [401]  1.935  2.295  1.880  3.747  4.277  3.346  4.987  4.750  5.731  3.001
##  [411]  2.046  2.365  3.716  3.190  4.881  3.413  1.846  1.848  1.920  1.957
##  [421]  1.993  1.833  4.983  9.142  2.883  3.143  2.938  3.088  2.740  2.557
##  [431]  2.253  1.901  1.959  2.011  1.960  5.388  6.075  4.080  7.662  9.166
##  [441]  6.857  6.955  6.223  5.543  8.678 10.692  6.300  9.127  5.232  6.299
##  [451]  4.754 14.723  5.417 13.452  3.026 13.020  2.517  7.581  4.883  6.159
##  [461]  4.564  2.584  7.030  3.266  4.325  3.196  3.482  2.349  2.465  2.893
##  [471]  2.364  2.563  2.204  2.349  3.029  2.432  1.833  1.877  2.304  1.937
##  [481]  3.234  2.231  1.840  2.499  3.291  2.769  2.274  3.505  3.080  2.770
##  [491]  3.856  2.325  2.550  1.968  5.251  4.289  6.615  2.360  3.621  3.010
##  [501]  2.199  3.563  3.469  3.019  3.413  2.553  3.060  2.545  2.630  3.086
##  [511]  3.400  4.225  3.862  3.509  2.972  2.876  3.812  3.649  7.300  3.578
##  [521]  6.265  3.600  4.134  4.235  6.680  2.677  7.472  5.010  4.652  4.589
##  [531]  2.969  3.581  3.344  2.251  2.843  3.097  3.553  5.515  2.485  1.927
##  [541]  4.721  2.309  1.867  2.428  2.190  2.251  3.297  2.576  2.354  2.102
##  [551]  2.673  2.101  2.331  3.647  3.661  2.210  4.367  3.398  4.053  3.764
##  [561]  3.244  3.430  2.425  1.981  3.465  2.070  1.910  3.141  2.196  4.136
##  [571]  1.959  3.651  2.527  2.989  1.907  2.204  2.283  1.883 12.571  6.403
##  [581]  3.991  2.756  2.322  1.972  1.834  2.324  2.748  3.038  2.873  1.908
##  [591]  1.941  1.897  2.058  2.150  1.846  1.956  1.932  1.967  1.973  2.202
##  [601]  2.199  2.869  2.814  2.126  2.711  2.418  2.633  2.495  2.113  2.059
##  [611]  2.080  1.955  2.544  2.072  1.905  2.230  2.271  2.002  2.174  2.089
##  [621]  1.894  2.824  2.801  2.088  1.891  2.229  1.899  2.322  2.326  1.838
##  [631]  1.956  2.102  1.884  2.007  1.877  1.844  2.365  2.365  2.038  1.855
##  [641]  1.998  2.446  1.908  2.147  1.856  3.222  2.597  2.174  3.344  3.512
##  [651]  2.297  1.941  1.927  2.037  1.844  1.952  2.321  2.022  1.898  2.127
##  [661]  2.362  1.912  1.848  2.638  1.985  3.118  2.782  2.620  2.352  1.952
##  [671]  2.056  2.113  3.690  2.928  1.860  1.863  3.208  3.019  2.057  1.943
##  [681]  3.092  2.367  1.866  1.950  1.857  5.591  1.875  2.371  2.893  2.777
##  [691]  3.497  3.529  2.143  1.964  2.037  2.069  1.904  1.860  6.832  8.015
##  [701]  7.050  6.124  4.640  2.892  2.148  1.964  1.889  1.869  2.179  1.837
##  [711]  2.038  1.919  2.025  2.226  1.894  1.995  1.882  2.219  1.966  2.157
##  [721]  2.379  2.570  1.870  2.792  2.601  2.785  2.281  2.324  2.323  1.992
##  [731]  1.904  2.449  1.937  3.336  2.001  1.858  1.949  2.110  1.830  2.316
##  [741]  2.087  2.668  2.327  1.938  1.903  2.341  1.974  3.370  1.977  2.504
##  [751]  3.531  1.960  2.544  1.857  1.946  2.263  2.161  1.833  1.950  1.871
##  [761]  2.165  2.428  2.134  1.921  2.089  2.215  1.902  2.691  1.862  2.102
##  [771]  2.278  2.009  1.898  2.003  1.925  2.487  2.063  1.882  2.525  2.043
##  [781]  2.190  2.671  2.155  2.654  2.009  2.251  1.971  3.972  1.959  1.910
##  [791]  1.935  4.348  2.294  2.326  2.714  2.672  2.258  2.491  4.375  2.072
##  [801]  2.416  2.309  4.256  2.977  2.863  3.173  2.860  2.710  2.447  1.939
##  [811]  2.774  2.038  1.926  1.945  1.988  2.082  2.113  2.174  3.307  2.694
##  [821]  1.931  1.930  2.112  1.874  2.674  2.288  3.084  4.436  2.833  4.152
##  [831]  2.977  2.133  4.327  5.747  5.744  6.663  4.367  4.265  3.975  3.942
##  [841]  4.179  3.183  3.926  3.933  3.301  3.645  3.479  4.090  3.835  4.712
##  [851]  4.228  4.228  1.839  4.087  3.290  2.275  1.971  2.487  4.031  2.499
##  [861]  2.023  2.120  1.870  1.846  1.920  2.482  2.161  2.737  2.806  2.750
##  [871]  2.399  1.973  2.005  2.066  2.205  1.833  1.840  2.108  1.950  2.569
##  [881]  2.296  3.043  1.955  2.276  1.983  1.940  1.843  1.953  2.180  3.274
##  [891]  2.332  2.058  4.353  8.238  2.898  5.960  3.831  7.223  2.179  2.172
##  [901]  2.733  3.007  2.739  3.861  4.323  5.778  4.822  2.890  2.680  2.051
##  [911]  2.812  2.180  5.758  2.142  3.706  6.758  4.337 11.107  1.856  3.869
##  [921]  5.272  6.878  3.506  2.368  4.287  2.632  3.610  4.940  2.136 10.288
##  [931]  5.504  3.825  2.962  8.253  2.152  2.014  3.858  3.813  2.646  6.501
##  [941]  3.706  1.958  1.875  4.986  8.326  6.771  3.151  3.925  6.202  6.093
##  [951]  3.201  5.867  2.927  3.934  3.625  4.717  2.414  5.743  2.253  3.027
##  [961]  4.868  2.686  5.197  3.668  3.049  2.354  3.948  4.113  4.665  5.629
##  [971]  5.294  2.669  5.755  3.243  2.908  6.710  8.595  6.606  3.964  4.516
##  [981]  3.395  3.610  3.104  1.914  2.905  2.685  1.958  2.745  1.886  3.156
##  [991]  3.407  5.436  5.951  3.141  4.263  4.465  3.914  3.212  2.939  1.916
## [1001]  2.279  2.051  2.236  1.892  4.095  2.290  1.982  4.089  2.096  2.075
## [1011]  3.820  3.376  2.338  2.371  3.153  2.001  3.050  2.861  1.892  1.832
## [1021]  2.000  2.546  3.286  2.777  3.636  2.140  1.938  2.053  2.797  4.270
## [1031]  6.197  3.766  3.937  3.400  2.105  2.117  1.982  1.912  1.951  2.383
## [1041]  2.418  2.177  1.982  2.304  1.886  3.224  2.928  3.258  3.273  2.824
## [1051]  2.260  2.377  4.008  7.483 10.194  7.092  8.171  7.961  2.512  8.048
## [1061]  6.367  5.661  5.727  2.974  3.568  3.919  6.383  2.240  2.364  2.690
## [1071]  2.251  1.835  3.220  2.057  2.515  2.494  6.026  5.446  2.549  2.477
## [1081]  2.150  1.834  2.661  2.407  1.896  2.424  1.941  1.906  1.867  2.387
## [1091]  2.354  1.926  1.834  2.310  1.862  1.859  1.915  3.120  2.501  2.035
## [1101]  1.899  2.789  2.249  2.491  3.089  3.157  1.990  1.840  1.938  2.064
## [1111]  1.940  3.729  1.977  2.219  1.883  3.160  2.179  1.907  1.856  3.524
## [1121]  5.260  2.735  5.987  2.045  5.331  2.187  6.991  5.009  2.530  4.542
## [1131]  2.980  2.813  2.493  2.162  1.904  2.087  1.849  2.477  2.840  2.536
## [1141]  2.294  3.693  3.082  3.394  2.465  2.180  3.043  3.703  3.192  3.673
## [1151]  3.130  3.101  3.422  2.900  3.472  3.299  2.863  3.122  2.281  2.662
## [1161]  2.926  2.280  1.909  1.971  3.102  2.304  6.410  2.913  2.238  3.631
## [1171]  3.439  3.186  3.703  4.658  5.929  3.439  2.347  2.731  4.574  1.970
## [1181]  2.832  5.992  2.267  1.977  3.533  2.102  2.501  2.856  2.883  2.716
## [1191]  2.779  2.438  2.111  2.869  2.911  2.269  3.349  3.366  2.444  1.902
## [1201]  2.239  2.001  2.994  1.862  2.735  2.434  2.538  3.063  2.024  2.292
## [1211]  2.982  3.153  3.020  6.871  3.692  3.801  2.259  1.919  2.386  2.142
## [1221]  3.859  3.014  1.838  1.845  1.967  1.914  2.005  1.902  1.901  1.922
## [1231]  1.942  2.046  2.167  2.132  2.442  2.140  2.281  2.184  2.361  2.280
## [1241]  2.179  2.039  1.840  2.814  2.307  2.654  1.861  3.452  2.322  3.316
## [1251]  2.201  3.653  2.260  3.723  2.778  4.404  2.534  5.732  2.261  2.365
## [1261]  2.593  2.678  2.650  3.306  1.907  4.226  3.440  2.742  3.663  6.081
## [1271]  2.802  4.764  1.959  3.817  3.287  5.968  5.266  2.224  2.787  4.480
## [1281]  7.055  5.270  5.713  5.303  3.681  4.717  4.598  2.848  2.966  3.109
## [1291]  3.156  2.149  2.399  2.170  2.131  1.948  2.439  2.239  2.078  2.055
## [1301]  1.990  2.532  2.691  2.643  2.512  2.457  3.262  2.176  2.457  2.831
## [1311]  1.989  2.813  2.521  5.745  5.690  2.718  5.975  8.509  7.192  5.954
## [1321]  2.408  1.884  2.303  2.081  2.249  2.181  2.177  2.069  2.241  1.970
## [1331]  1.962  2.166  1.979  2.315  2.551  2.226  2.924  3.231  3.726  2.565
## [1341]  4.009  3.730  2.471  2.132  2.102  2.178  2.066  2.401  2.106  2.309
## [1351]  2.154  2.369  1.878  2.413  2.318  2.137  1.941  2.042  3.060  2.166
## [1361]  3.072  2.008  3.622  2.164  3.535  2.312  3.368  2.348  3.537  2.827
## [1371]  4.070  2.231  2.893  1.854  2.252  3.087  2.584  3.095  2.072  4.020
## [1381]  4.407  4.442  4.619  3.443  7.920  9.410  1.980  2.305  3.841  2.924
## [1391]  3.970  3.106  6.181  5.258  3.770  8.613  3.881  2.934  2.011  3.122
## [1401]  1.884  2.828  4.406  2.369  2.604  2.510  2.178  2.037  2.058  2.074
## [1411]  2.160  2.075  1.978  2.756  2.973  3.319  3.690  3.336  3.755  2.869
## [1421]  2.280  3.990  5.722  3.290  5.035  3.311  5.308  2.129  2.087  2.243
## [1431]  1.983  1.984  2.127  2.633  2.665  2.179  2.992  3.018  2.211  1.843
## [1441]  5.192  1.953  3.112  1.884  2.699  9.000  8.350  2.638  3.236  2.681
## [1451]  4.854  3.304  3.152  2.870  2.058  7.328  2.193  2.184  2.098  2.215
## [1461]  2.111  1.920  2.273  1.941  1.944  2.131  2.139  2.278  2.942  2.283
## [1471]  1.920  1.985  1.835  2.255  1.987  3.135  1.931  2.576  1.902  2.239
## [1481]  2.182  1.950  2.313  2.120  1.864  2.419  2.256  1.828  2.019  1.895
## [1491]  2.140  1.846  2.106  1.953  1.834  2.236  2.171  2.256  1.913  1.850
## [1501]  3.027  2.218  2.290  2.032  2.181  1.878  1.896  2.384  2.134  2.279
## [1511]  1.941  2.400  2.278  1.988  2.274  2.705  1.944  2.351  2.342  1.958
## [1521]  1.886  2.794  2.263  2.169  2.056  2.267  2.176  1.938  1.929  2.170
## [1531]  2.852  2.239  2.031  2.272  2.199  1.894  2.116  2.411  2.157  1.974
## [1541]  2.290  1.996  2.256  2.155  2.115  2.120  2.194  2.232  1.862  2.180
## [1551]  2.582  2.145  1.970  2.186  2.167  1.847  2.275  1.972  1.984  2.075
## [1561]  1.941  1.900  2.459  1.965  1.860  2.142  1.993  1.936  1.925  1.960
## [1571]  2.246  2.264  2.437  1.897  2.211  2.753  1.969  2.554  4.832  3.098
## [1581]  2.500  1.902  3.463  3.322  2.462  4.689  2.104  3.951  1.916  5.188
## [1591]  9.283  5.285  7.480  5.268  4.734  2.401  6.784  7.548  8.372  7.651
## [1601]  3.549  2.562  3.470  4.713  3.940  4.456  2.341  2.084  2.041  2.859
## [1611]  2.585  2.550  2.396  2.935  2.560  2.506  2.977  3.008  2.307  2.109
## [1621]  2.921  2.733  4.751  3.289  2.159  1.947  3.015  1.915  2.112  1.964
## [1631]  2.690  2.701  2.051  2.335  1.938  2.169  2.958  2.479  2.556  2.553
## [1641]  1.994  2.595  2.454  2.907  3.825  3.501  3.662  2.434  2.087  1.935
## [1651]  2.410 11.112  3.289  3.364  2.305  3.743  2.620  4.056 14.143  2.270
## [1661]  1.969  2.286  2.230  1.947  2.661  3.824  2.356  3.138  1.922  1.903
## [1671]  2.543  2.018  2.163  2.507  3.497  3.223  1.850  2.221  2.281  1.937
## [1681]  1.903  2.011  2.030  2.222  2.025  1.828  2.229  1.910  2.058  2.415
## [1691]  2.496  2.582  2.549  3.221  3.198  2.659  2.393  6.273  2.560  2.291
## [1701]  3.591  1.873  2.307  1.937  1.934  2.646  3.347  2.052  2.665  3.201
## [1711]  2.609  2.043  2.165  2.171  2.022  2.569  2.163  2.233  2.049  1.927
## [1721]  2.208  4.605  3.304  2.901  1.943  2.601  2.368  1.868  2.747  1.943
## [1731]  2.052  2.483  2.226  7.170  2.647  3.872  5.113  2.936  4.079  2.179
## [1741]  2.477  1.868  2.635  1.960  2.145  2.212  2.686  2.038  2.137  2.010
## [1751]  2.391  2.464  1.999  1.863  1.840  2.308  1.849  2.171  3.566  5.018
## [1761]  2.881  2.006  1.873  1.869  1.890  1.864  2.140  2.259  1.885  2.351
## [1771]  2.108  2.487  2.155  2.126  3.545  3.784  2.508  1.966  1.880  1.833
## [1781]  1.907  2.437  2.673  2.132  1.843  2.303  2.015  2.051  2.386  1.955
## [1791]  1.895  1.838  1.832  2.230  2.149  2.604  2.135  2.264  1.892  1.863
## [1801]  2.135  1.852  2.282  1.900  1.922  2.072  2.015  1.955  1.938  2.179
## [1811]  2.046  2.070  2.151  2.185  2.105  2.263  2.123  2.071  2.054  2.187
## [1821]  1.833  1.990  1.925  1.928  1.837  2.977  2.249  3.306  2.323  2.206
## [1831]  1.871  1.829  2.283  2.336  2.027  2.280  2.356  2.029  2.218  2.260
## [1841]  2.626  2.313  3.677  2.748  1.906  2.214  2.158  2.021  1.933  2.127
## [1851]  2.056  2.055  2.818  2.479  2.090  2.766  2.030  1.897  2.065  2.632
## [1861]  1.992  2.011  2.409  1.927  1.870  2.296  1.906  1.842  1.907  2.175
## [1871]  2.606  2.086  1.848  1.903  1.912  2.878  1.880  1.884  2.170  1.985
## [1881]  2.194  2.162  1.982  1.995  2.050  2.282  1.924  2.134  2.100  2.212
## [1891]  2.244  2.230  2.283  2.161  1.964  1.893  2.134  1.863  1.936  1.936
## [1901]  2.198  2.070  2.981  1.949  2.122  1.923  2.027  1.973  3.191  3.424
## [1911]  3.158  3.116  3.439  2.335  2.040  1.842  1.980  2.270  3.220  2.252
## [1921]  2.905  2.041  2.809  3.249  3.040  2.005  2.242  6.419  2.480  3.608
## [1931]  2.917  7.595  2.294  4.346  2.149  2.994  2.159  5.007  4.290  5.729
## [1941]  6.758  3.881  4.552  2.657  3.757  7.285  4.808  3.424  4.868  2.899
## [1951]  3.431  3.986  6.578  2.155  2.754  1.850  2.488  2.237  2.340  2.265
## [1961]  2.243  2.335  2.407  1.828  2.474  1.890  2.221  2.110  2.284  2.179
## [1971]  2.415  2.181  2.111  2.252  2.011  2.093  2.073  2.205  2.050  3.258
## [1981]  2.664  2.077  2.387  1.894  4.319  3.300  2.872  4.088  2.790  3.351
## [1991]  1.925  4.061  9.722  6.436  7.040  7.093  6.753  7.974 11.149  5.494
## [2001]  4.417  7.775  3.139  7.882  6.483  5.957  6.503  3.114  2.082  2.318
## [2011]  2.262  2.070  2.683  2.112  1.988  2.609  2.168  2.096  2.234  2.241
## [2021]  2.334  1.862  2.165  1.979  1.999  2.323  2.208  2.038  2.108  1.986
## [2031]  1.862  2.244  3.032  3.194  5.279  4.739  2.027  4.129  2.561  3.269
## [2041]  4.189  2.681  1.993  2.791  3.215  4.315  5.551  5.477  4.364  2.829
## [2051]  3.659  3.144  8.605  2.874  2.364  2.821  2.116  7.618  4.648  8.875
## [2061]  7.401  7.650  3.922  5.944  2.072  2.242  2.465  2.078  1.872  2.071
## [2071]  1.988  1.831  2.185  2.166  1.864  1.887  2.045  2.290  2.097  2.167
## [2081]  1.959  2.725  2.360  2.683  7.266  2.602  2.552  9.255  3.083  6.517
## [2091]  5.857  4.672  8.100  1.992  2.044  4.335  2.551  3.069  3.020  2.032
## [2101]  2.029 11.975  3.568  2.704  2.313  9.652  2.231  4.648  5.858  6.964
## [2111]  4.225  3.395  2.763  2.698  5.374  2.130  4.621  3.821  5.233  6.187
## [2121]  6.514  4.849  4.905  3.598  5.273  5.458  4.593  6.036  2.021  1.870
## [2131]  1.888  2.182  1.909  1.909  2.105  2.827  2.746  2.255  3.480  5.813
## [2141]  8.022  6.582  5.747  6.456  6.995  6.768  7.067  6.423  4.862  5.624
## [2151]  5.636  6.363  7.289  3.677  2.475  2.521  2.289  1.844  2.075  1.967
## [2161]  2.161  1.868  1.920  2.614  1.947  2.101 10.786  6.310  6.884  5.578
## [2171]  6.710  7.002  3.200  3.147  5.500  2.516  3.878  4.383  6.587  7.341
## [2181]  2.817  5.978  3.365  2.060  1.994  1.870  3.879  2.038  2.265  2.099
## [2191]  2.149  1.954  2.059  1.973  2.024  1.862  1.885  2.088  2.055  1.922
## [2201]  2.003  1.917  6.133  1.869  1.944  1.858  1.910  1.993  1.906  1.919
## [2211]  1.885  1.948  2.038  2.014  2.018  2.083  2.004  1.856  2.059  4.520
## [2221]  2.067  2.109  1.969  1.828  1.951  2.090  2.822  1.952  1.868  2.027
## [2231]  1.841  1.842  2.004  1.983  1.928  1.882  1.936  2.181
rem_out1_kud95 <- subset(week_kuds, KUD95 < 1.827)
summary(rem_out1_kud95)
##                       File          Species            Transmitter   
##  Seriola_rivoliana      : 2676   Dsar   :3608   A69-1008-104 :  283  
##  Epinephelus_marginatus1: 2053   Gmor   :3041   A69-1008-102 :  281  
##  Gadus_morhua1          : 1555   Emar   :2993   A69-1008-103 :  264  
##  Diplodus_sargus4       : 1469   Sriv   :2676   A69-1008-100 :  261  
##  Pseudocaranx_dentex    : 1305   Svir   :1533   A69-1303-6685:  249  
##  Sphyraena_viridensis1  : 1263   Pden   :1305   A69-1008-101 :  247  
##  (Other)                :13051   (Other):8216   (Other)      :21787  
##      KUD50            KUD95          WeekYear                    Habitat     
##  Min.   :0.1620   Min.   :0.7600   Length:23372       benthopelagic  : 8090  
##  1st Qu.:0.1640   1st Qu.:0.7620   Class :character   demersal       :10887  
##  Median :0.1690   Median :0.7935   Mode  :character   pelagic-neritic: 4395  
##  Mean   :0.1984   Mean   :0.9441                                             
##  3rd Qu.:0.2130   3rd Qu.:1.0550                                             
##  Max.   :0.5020   Max.   :1.8260                                             
##                                                                              
##          Migration      ComImport       Length_cm        LengthStd     
##  non-migratory:13387   high  :14579   Min.   : 12.20   Min.   :0.1375  
##  oceanodromous: 9985   medium: 8593   1st Qu.: 32.50   1st Qu.:0.3451  
##                        minor :  200   Median : 45.50   Median :0.5000  
##                                       Mean   : 49.65   Mean   :0.4873  
##                                       3rd Qu.: 62.00   3rd Qu.:0.6017  
##                                       Max.   :118.00   Max.   :1.0200  
##                                                                        
##     BodyMass        BodyMassStd         Longevity     Vulnerability  
##  Min.   :   19.7   Min.   :0.001964   Min.   : 8.00   Min.   :35.90  
##  1st Qu.:  503.6   1st Qu.:0.051925   1st Qu.:13.50   1st Qu.:63.40  
##  Median : 1134.0   Median :0.118734   Median :22.70   Median :66.30  
##  Mean   : 2479.0   Mean   :0.176874   Mean   :25.08   Mean   :64.65  
##  3rd Qu.: 2831.0   3rd Qu.:0.228268   3rd Qu.:29.00   3rd Qu.:71.50  
##  Max.   :22826.3   Max.   :1.490344   Max.   :60.00   Max.   :90.00  
##                                                                      
##      Troph       ReceiverDensity   MonitArea_km2       MCP_km2         
##  Min.   :2.860   Min.   :  0.030   Min.   : 0.690   Min.   :   0.1447  
##  1st Qu.:3.470   1st Qu.:  0.130   1st Qu.: 1.200   1st Qu.:   2.0148  
##  Median :4.090   Median :  0.330   Median : 3.700   Median :  12.2923  
##  Mean   :3.926   Mean   :  9.651   Mean   : 6.226   Mean   : 246.8819  
##  3rd Qu.:4.270   3rd Qu.: 12.410   3rd Qu.: 8.380   3rd Qu.: 355.9882  
##  Max.   :4.660   Max.   :131.310   Max.   :15.620   Max.   :2990.2885  
##                                                                        
##    NReceivers    MaxDistReceivers   MaxLength      MaxBodyMass   
##  Min.   : 4.00   Min.   : 0.53    Min.   : 38.0   Min.   :  585  
##  1st Qu.:15.00   1st Qu.: 3.21    1st Qu.: 55.0   1st Qu.: 2200  
##  Median :26.00   Median : 9.25    Median :122.0   Median :14300  
##  Mean   :39.93   Mean   :33.35    Mean   :115.5   Mean   :32029  
##  3rd Qu.:45.00   3rd Qu.:74.03    3rd Qu.:177.6   3rd Qu.:60000  
##  Max.   :98.00   Max.   :87.39    Max.   :200.0   Max.   :96000  
##                                                                  
##        a                 b         SpawnSeason              Tp        
##  Min.   :0.00391   Min.   :2.801   Length:23372       Min.   :   2.0  
##  1st Qu.:0.00900   1st Qu.:2.913   Class :character   1st Qu.: 293.0  
##  Median :0.01010   Median :3.060   Mode  :character   Median : 417.0  
##  Mean   :0.01429   Mean   :3.029                      Mean   : 753.4  
##  3rd Qu.:0.01802   3rd Qu.:3.120                      3rd Qu.:1467.0  
##  Max.   :0.09550   Max.   :3.280                      Max.   :2413.0  
##                                                                       
##        RI            ROM_mh            Week            Year      Spawn     
##  Min.   :0.000   Min.   :  0.00   37     :  529   2008   :3847   A : 1025  
##  1st Qu.:0.630   1st Qu.:  7.60   38     :  522   2011   :3527   SS:21108  
##  Median :0.810   Median : 19.40   35     :  508   2021   :2211   W : 1239  
##  Mean   :0.752   Mean   : 30.47   36     :  507   2009   :2020             
##  3rd Qu.:0.940   3rd Qu.: 37.80   39     :  507   2007   :1851             
##  Max.   :1.000   Max.   :508.80   29     :  505   2010   :1487             
##                                   (Other):20294   (Other):8429
boxplot(rem_out1_kud95$KUD95, col = "deepskyblue", ylab="KUD95", main="Boxplot of KUD95 after the first outliers removal")

boxplot.stats(week_kuds$KUD50) #superior limit where outliers start is 0.351
## $stats
## [1] 0.162 0.164 0.173 0.239 0.351
## 
## $n
## [1] 25612
## 
## $conf
## [1] 0.1722595 0.1737405
## 
## $out
##    [1] 0.575 0.361 0.469 0.473 0.490 0.510 0.360 0.528 0.463 0.469 0.521 0.444
##   [13] 0.467 0.477 0.494 0.393 0.463 0.480 0.469 0.490 0.456 0.417 0.423 0.422
##   [25] 0.470 0.388 0.478 0.426 0.441 0.466 0.458 0.614 0.454 0.464 0.433 0.503
##   [37] 0.467 0.428 0.385 0.395 0.395 0.474 0.405 0.436 0.458 0.406 0.374 0.364
##   [49] 0.375 0.454 0.408 0.556 0.497 0.365 0.473 0.380 0.468 0.358 0.426 0.389
##   [61] 0.376 0.367 0.401 0.483 0.455 0.365 0.467 0.574 0.591 0.588 0.374 0.574
##   [73] 0.435 0.545 0.737 0.414 0.500 0.408 0.400 0.435 0.383 0.371 0.365 0.381
##   [85] 0.370 0.352 0.386 0.432 0.401 0.365 0.381 0.390 0.365 0.377 0.393 0.464
##   [97] 0.370 0.429 0.457 0.407 0.391 0.405 0.414 0.401 0.373 0.384 0.393 0.417
##  [109] 0.393 0.488 0.431 0.422 0.522 0.488 0.367 0.479 0.394 0.360 0.371 0.364
##  [121] 0.390 0.366 0.360 0.387 0.457 0.385 0.356 0.492 0.438 0.416 0.479 0.363
##  [133] 0.569 0.447 0.428 0.426 0.445 0.361 0.418 0.532 0.466 0.396 0.424 0.377
##  [145] 0.400 0.375 0.410 0.375 0.386 0.395 0.446 0.510 0.379 0.418 0.396 0.397
##  [157] 0.442 0.463 0.360 0.489 0.475 0.465 0.359 0.484 0.420 0.375 0.387 0.531
##  [169] 0.597 0.530 0.385 0.563 0.536 0.437 0.616 0.377 0.595 0.414 0.492 0.502
##  [181] 0.519 0.538 0.579 0.359 0.382 0.369 0.440 0.402 0.474 0.574 1.394 1.494
##  [193] 1.843 0.560 0.477 0.626 0.999 0.792 1.046 0.981 0.709 0.857 0.385 0.383
##  [205] 0.405 0.498 0.361 0.582 0.555 0.500 0.402 0.574 0.568 0.449 0.438 0.583
##  [217] 0.375 0.568 0.370 0.548 0.413 0.429 0.435 0.379 0.477 0.420 0.463 0.473
##  [229] 0.426 0.424 0.437 0.359 0.427 0.408 0.372 0.974 0.859 0.566 0.369 0.374
##  [241] 0.527 0.617 0.395 0.450 0.514 0.364 0.390 0.356 0.441 0.399 0.367 0.389
##  [253] 0.384 0.447 0.490 0.463 0.368 0.529 0.433 0.395 0.371 0.428 0.453 0.449
##  [265] 0.478 0.686 0.611 0.459 0.529 0.352 0.461 0.408 0.616 0.458 0.387 0.548
##  [277] 0.682 0.402 0.624 0.420 0.413 0.713 0.408 0.608 0.701 0.682 0.464 0.465
##  [289] 0.459 0.489 0.371 0.617 0.418 0.588 0.398 0.408 0.628 0.491 0.745 0.418
##  [301] 0.407 0.627 0.432 0.683 0.379 0.367 0.749 0.636 0.783 0.381 0.407 0.406
##  [313] 0.565 0.679 0.580 0.365 0.394 0.472 0.362 0.355 0.450 0.424 0.528 0.406
##  [325] 0.427 0.400 0.448 0.388 0.466 0.360 0.368 0.466 0.827 0.400 0.400 0.380
##  [337] 0.400 0.498 0.374 0.352 0.376 0.486 0.448 0.419 0.429 0.371 0.409 0.416
##  [349] 0.412 0.404 0.489 0.632 0.428 0.356 0.528 0.748 0.456 1.049 0.620 0.444
##  [361] 0.669 0.582 0.620 0.602 0.767 0.833 0.734 0.523 0.623 0.382 0.503 0.362
##  [373] 0.403 0.441 0.445 0.456 0.492 0.479 0.485 0.642 0.575 0.489 1.126 0.394
##  [385] 0.736 0.686 0.471 0.724 0.534 0.611 0.379 1.186 0.439 0.484 0.460 0.742
##  [397] 0.817 0.920 1.098 0.698 0.442 0.467 0.361 0.835 0.357 0.779 0.389 0.721
##  [409] 0.781 0.776 0.443 0.870 0.799 0.736 0.424 0.664 0.671 0.427 0.404 0.361
##  [421] 0.441 0.507 0.740 0.694 0.666 0.627 0.528 0.432 0.362 0.425 0.403 0.407
##  [433] 0.464 0.472 0.410 0.381 0.519 0.453 0.498 0.684 0.528 1.271 1.429 0.620
##  [445] 0.389 0.366 0.959 0.718 0.427 0.685 0.550 1.510 0.457 0.665 0.374 0.431
##  [457] 0.392 0.402 0.374 0.419 0.589 0.455 0.360 0.509 0.388 0.869 0.684 0.545
##  [469] 0.400 0.388 0.469 0.741 0.840 0.668 1.089 1.092 0.994 0.543 0.524 0.903
##  [481] 0.370 0.536 0.948 0.406 0.372 0.405 0.379 0.439 0.370 0.375 0.419 0.415
##  [493] 0.429 0.374 0.369 0.448 0.354 0.360 0.408 0.430 0.458 0.406 0.406 0.369
##  [505] 0.431 0.377 0.402 0.381 0.530 0.449 0.486 0.478 0.362 0.398 1.148 0.564
##  [517] 0.458 0.503 0.540 0.614 0.686 0.474 0.491 0.397 0.443 0.804 1.049 0.423
##  [529] 1.496 0.959 1.009 0.511 1.330 1.166 1.510 2.561 0.809 1.382 0.565 0.711
##  [541] 0.645 3.104 0.564 2.383 0.487 1.296 0.465 0.506 1.168 1.557 1.180 0.613
##  [553] 0.941 0.815 0.525 0.377 0.423 0.376 0.381 0.370 0.382 0.388 0.364 0.378
##  [565] 0.379 0.455 0.526 0.419 0.361 0.447 0.378 0.423 0.384 0.452 0.416 0.353
##  [577] 0.646 0.449 0.354 0.617 0.377 0.426 0.353 0.513 0.632 0.494 0.466 0.903
##  [589] 0.522 0.602 0.702 0.442 0.437 0.667 0.693 0.666 0.564 0.470 0.717 0.691
##  [601] 0.914 0.767 0.741 0.582 0.526 0.819 0.712 1.441 0.480 1.140 0.758 0.528
##  [613] 0.366 0.519 0.690 0.458 0.882 0.663 0.715 0.806 0.615 0.509 0.635 0.549
##  [625] 0.556 0.562 0.637 0.705 0.604 0.524 0.831 0.352 0.430 0.402 0.377 0.415
##  [637] 0.557 0.407 0.710 0.465 0.625 0.398 0.715 0.423 0.505 0.615 0.911 0.739
##  [649] 0.734 0.778 0.668 0.546 0.641 0.618 0.479 0.709 0.565 0.497 0.734 0.542
##  [661] 1.003 0.486 0.838 0.615 0.519 0.389 0.379 0.376 0.404 0.406 0.374 0.374
##  [673] 0.379 0.382 1.377 0.832 0.988 0.353 0.390 0.399 0.374 0.361 0.397 0.479
##  [685] 0.371 0.394 0.365 0.369 0.372 0.353 0.394 0.451 0.378 0.503 0.388 0.512
##  [697] 0.357 0.416 0.384 0.464 0.464 0.479 0.398 0.356 0.362 0.509 0.508 0.766
##  [709] 0.516 0.645 0.502 0.556 0.442 0.514 0.381 0.500 0.603 0.383 0.398 0.457
##  [721] 0.422 0.482 0.693 0.580 0.374 0.453 0.356 0.396 0.445 0.499 0.357 0.406
##  [733] 0.362 0.384 0.420 0.465 0.414 0.354 0.437 0.374 0.353 0.385 0.399 0.415
##  [745] 0.418 0.433 0.404 0.380 0.406 0.390 0.378 0.369 0.411 0.356 0.374 0.358
##  [757] 0.368 0.373 0.355 0.396 0.402 0.375 0.372 0.378 0.398 0.382 0.370 0.355
##  [769] 0.416 0.420 0.438 0.449 0.415 0.395 0.396 0.360 0.413 0.367 0.361 0.435
##  [781] 0.438 0.363 0.439 0.356 0.368 0.659 0.509 0.368 0.618 0.374 0.366 0.575
##  [793] 0.357 0.371 0.353 0.369 0.355 0.387 0.475 0.352 0.488 0.498 0.365 0.357
##  [805] 0.361 0.564 0.411 0.530 0.453 0.360 0.484 0.370 0.689 0.368 0.419 0.379
##  [817] 0.352 0.639 0.624 0.405 0.352 0.408 0.358 0.424 0.485 0.500 0.509 0.427
##  [829] 0.463 0.462 0.429 0.371 0.412 0.358 0.368 0.485 0.886 0.556 0.570 0.393
##  [841] 0.404 0.363 0.384 0.396 0.360 0.528 0.396 0.384 0.390 0.378 0.562 0.386
##  [853] 0.370 0.408 0.355 0.396 0.370 0.376 0.395 0.358 0.474 0.361 0.379 0.372
##  [865] 0.542 0.365 0.359 0.435 0.353 0.431 0.371 0.449 0.402 0.427 0.439 0.492
##  [877] 0.459 0.362 0.356 0.357 0.367 0.352 0.377 0.370 0.375 0.423 0.367 0.386
##  [889] 0.379 0.380 0.376 0.395 0.482 0.396 0.377 0.451 0.727 0.636 0.354 0.359
##  [901] 0.368 0.387 0.769 0.364 0.396 0.360 0.373 0.436 0.609 0.440 0.432 0.703
##  [913] 0.534 0.489 0.706 0.478 0.867 0.378 0.379 0.546 0.377 0.465 0.497 0.423
##  [925] 0.495 0.514 0.449 0.399 0.437 0.370 0.381 0.352 0.371 0.463 0.375 0.597
##  [937] 0.455 0.403 0.352 0.423 0.369 0.437 0.359 0.380 0.750 0.641 0.421 0.460
##  [949] 0.572 0.446 0.461 0.448 0.392 0.625 0.511 0.752 0.422 0.689 0.367 0.464
##  [961] 0.570 0.390 0.399 0.377 0.508 0.569 0.413 0.508 0.429 0.533 0.529 0.435
##  [973] 0.595 0.562 0.562 0.357 0.398 0.373 0.367 0.353 0.408 0.413 0.475 0.688
##  [985] 0.394 0.500 0.434 0.489 0.472 0.440 0.623 0.457 0.470 0.488 0.408 0.417
##  [997] 0.499 0.458 0.520 0.449 0.469 0.453 0.357 0.412 0.366 0.425 0.354 0.430
## [1009] 0.370 0.377 0.352 0.427 0.649 0.465 0.486 0.477 0.472 0.380 0.372 0.410
## [1021] 0.439 0.406 0.468 0.440 0.358 0.352 0.363 0.484 0.498 0.356 0.592 0.384
## [1033] 0.366 0.358 0.380 0.358 0.352 0.501 0.409 0.439 0.458 0.486 0.477 0.357
## [1045] 0.573 0.492 0.706 0.607 0.492 0.536 0.426 0.360 0.407 0.405 0.773 0.373
## [1057] 0.504 0.389 0.372 0.521 0.663 0.382 0.593 1.032 0.737 0.466 0.430 0.740
## [1069] 1.139 0.969 0.844 0.886 0.556 0.854 0.610 0.604 0.765 0.594 0.569 0.884
## [1081] 0.932 0.935 1.141 0.929 0.841 0.409 0.743 0.858 0.383 0.482 0.502 1.032
## [1093] 0.546 0.511 0.470 0.378 0.367 0.415 0.394 0.427 0.530 0.499 0.435 0.467
## [1105] 0.386 0.424 0.389 0.432 0.379 0.432 0.575 0.471 0.411 0.544 0.453 0.423
## [1117] 0.383 0.644 0.376 0.409 1.002 1.644 0.609 1.024 0.366 0.830 1.231 0.465
## [1129] 0.462 0.385 0.407 0.713 0.524 0.486 0.430 1.243 0.934 0.620 0.467 0.529
## [1141] 0.379 0.707 0.728 1.108 0.730 1.815 0.362 0.588 0.525 1.322 0.373 0.520
## [1153] 0.444 0.578 1.563 1.095 0.840 0.611 0.804 0.701 0.805 0.977 0.580 0.452
## [1165] 0.357 0.386 0.377 1.584 1.109 0.531 0.722 1.350 0.842 0.586 1.062 0.393
## [1177] 0.382 0.564 0.867 0.861 0.562 0.745 1.095 0.901 0.456 0.465 0.696 0.641
## [1189] 0.882 0.919 0.854 0.569 0.533 0.914 1.505 0.782 0.731 0.890 0.904 0.644
## [1201] 0.702 0.430 0.666 0.429 0.440 0.580 0.470 1.003 0.865 0.395 0.891 0.754
## [1213] 0.497 0.604 0.490 0.762 0.499 0.390 0.381 0.372 0.739 0.424 0.409 0.499
## [1225] 0.531 0.577 0.737 0.714 0.683 0.364 0.370 0.352 0.355 0.390 0.617 0.857
## [1237] 0.485 0.496 0.430 0.413 0.430 0.522 0.489 0.406 0.354 0.730 0.539 0.759
## [1249] 0.708 0.701 0.387 0.491 1.129 0.445 0.619 0.505 0.703 0.356 0.409 0.387
## [1261] 0.360 0.465 0.675 0.460 0.447 0.420 0.407 0.500 0.526 0.927 0.947 0.374
## [1273] 0.377 0.484 0.385 0.410 0.492 0.413 0.413 0.395 0.409 0.389 0.494 0.462
## [1285] 0.414 0.412 0.376 0.457 0.368 0.374 0.570 0.375 0.412 0.549 0.550 0.803
## [1297] 0.473 0.434 0.397 0.373 0.839 0.420 0.355 0.650 0.458 0.380 0.522 0.373
## [1309] 0.608 0.563 0.532 0.462 0.796 0.473 0.829 0.467 0.520 0.554 0.587 0.671
## [1321] 0.422 0.413 0.362 0.557 0.354 0.352 0.606 0.443 0.366 0.819 0.439 0.838
## [1333] 0.547 0.490 0.965 0.690 0.788 0.603 0.533 0.607 0.732 0.709 0.773 0.405
## [1345] 0.754 0.432 0.524 0.630 0.449 0.518 0.366 0.425 0.422 0.535 0.528 0.674
## [1357] 0.714 0.403 0.482 0.406 0.357 0.361 0.405 0.513 0.375 0.457 0.417 0.826
## [1369] 0.425 0.546 0.740 0.615 0.524 0.521 0.627 0.671 0.724 0.358 0.477 0.365
## [1381] 0.434 0.608 0.479 0.508 0.627 0.436 0.560 0.524 0.595 0.542 0.615 0.624
## [1393] 0.378 0.400 0.497 0.413 0.481 0.420 0.429 0.407 0.435 0.434 0.436 0.455
## [1405] 0.424 0.411 0.407 0.444 0.435 0.402 0.424 0.384 0.417 0.403 0.362 0.397
## [1417] 0.376 0.387 0.470 0.411 0.489 0.380 0.360 0.362 0.354 0.387 0.392 0.501
## [1429] 0.354 0.589 0.366 0.556 0.522 0.425 0.462 0.813 0.545 0.616 0.794 0.459
## [1441] 0.540 0.665 0.466 0.590 0.574 0.751 0.484 0.803 0.384 0.457 0.559 0.469
## [1453] 0.549 0.668 0.688 0.367 0.802 0.484 0.598 1.067 0.412 0.553 0.361 0.358
## [1465] 0.681 0.420 0.822 0.579 0.424 0.523 0.532 1.013 0.768 0.848 0.835 0.852
## [1477] 0.909 0.797 0.415 0.682 0.515 0.372 0.461 0.386 0.537 0.352 0.461 0.439
## [1489] 0.557 0.522 0.550 0.588 0.675 0.680 0.728 0.476 0.418 0.498 0.400 0.394
## [1501] 0.409 0.437 0.618 0.445 1.277 1.229 0.597 1.118 2.046 0.811 0.754 0.401
## [1513] 0.378 0.364 0.438 0.353 0.491 0.414 0.481 0.399 0.546 0.479 0.584 0.573
## [1525] 0.601 0.721 0.826 0.465 0.393 0.409 0.535 0.391 0.563 0.530 0.417 0.542
## [1537] 0.559 0.475 0.737 0.471 0.651 0.845 0.463 0.593 0.623 0.486 0.596 0.599
## [1549] 0.884 0.510 0.624 0.501 0.661 0.577 0.561 0.388 0.676 0.929 0.589 0.576
## [1561] 0.586 0.842 1.132 0.374 0.459 0.889 0.533 0.434 0.553 1.126 1.143 0.712
## [1573] 0.931 0.776 0.548 0.384 0.535 0.487 0.841 0.416 0.362 0.496 0.455 0.355
## [1585] 0.489 0.517 0.485 0.525 0.728 0.634 0.789 0.875 0.625 0.529 0.631 0.511
## [1597] 0.362 0.580 0.889 0.696 0.823 0.681 0.635 0.451 0.479 0.449 0.391 0.610
## [1609] 0.375 0.462 0.394 1.086 0.431 0.607 0.358 0.598 2.022 1.748 0.431 0.702
## [1621] 0.732 0.983 0.395 0.499 1.275 0.395 0.382 0.356 0.484 0.379 0.383 0.490
## [1633] 0.569 0.461 0.472 0.443 0.400 0.356 0.387 0.378 0.463 0.388 0.377 0.427
## [1645] 0.394 0.461 0.637 0.415 0.499 0.412 0.369 0.392 0.489 0.487 0.481 0.427
## [1657] 0.357 0.511 0.471 0.369 0.475 0.398 0.381 0.480 0.439 0.481 0.402 0.385
## [1669] 0.430 0.397 0.360 0.500 0.456 0.366 0.381 0.384 0.399 0.433 0.436 0.352
## [1681] 0.385 0.406 0.482 0.398 0.360 0.504 0.412 0.575 0.388 0.524 0.423 0.385
## [1693] 0.357 0.362 0.369 1.109 0.654 0.564 0.388 0.478 0.676 0.641 0.746 0.862
## [1705] 2.086 1.283 1.586 1.243 0.911 1.584 1.096 0.911 1.493 0.520 0.537 0.576
## [1717] 0.453 0.830 0.557 0.361 0.489 0.424 0.362 0.523 0.545 0.440 0.452 0.585
## [1729] 0.461 0.545 0.598 0.560 0.428 0.426 0.629 0.397 0.430 0.489 0.578 0.405
## [1741] 0.518 0.404 0.388 0.492 0.565 0.439 0.462 0.467 0.368 0.579 0.745 0.721
## [1753] 0.644 0.460 0.365 0.456 2.034 0.381 0.424 0.435 0.413 0.682 3.278 0.355
## [1765] 0.373 0.367 0.365 0.820 0.477 0.638 0.375 0.378 0.454 0.365 0.414 0.361
## [1777] 0.398 0.425 0.597 0.373 0.352 0.520 0.368 0.366 0.362 0.402 0.586 0.499
## [1789] 0.359 0.486 0.403 0.502 0.367 0.394 0.391 0.430 0.382 0.437 0.442 0.358
## [1801] 0.404 0.359 0.393 0.358 0.385 0.428 0.431 0.505 0.583 0.726 0.620 0.733
## [1813] 0.723 0.680 0.425 0.877 0.473 0.359 0.851 0.357 0.408 0.396 0.498 0.444
## [1825] 0.465 0.388 0.505 0.442 0.730 0.816 0.490 0.559 0.584 0.390 0.426 0.494
## [1837] 0.498 0.357 0.360 0.413 0.605 0.718 0.512 0.380 0.369 0.580 0.367 0.353
## [1849] 0.380 0.455 0.415 0.761 0.357 0.402 0.375 1.305 0.352 0.466 0.644 0.735
## [1861] 0.840 0.631 0.521 0.376 0.425 0.450 0.457 0.422 0.358 0.359 0.364 0.357
## [1873] 0.398 0.401 0.366 0.490 0.510 0.603 0.457 0.476 0.359 0.376 0.452 0.398
## [1885] 0.353 0.382 0.540 1.085 0.650 0.399 0.475 0.378 0.408 0.368 0.352 0.493
## [1897] 0.470 0.360 0.404 0.598 0.470 0.606 0.542 0.695 0.461 0.569 0.471 0.755
## [1909] 0.611 0.460 0.386 0.372 0.450 0.419 0.378 0.465 0.520 0.391 0.523 0.655
## [1921] 0.501 0.422 0.435 0.396 0.352 0.401 0.435 0.547 0.466 0.645 0.438 0.454
## [1933] 0.427 0.383 0.410 0.406 0.465 0.389 0.502 0.394 0.430 0.387 0.435 0.385
## [1945] 0.360 0.398 0.484 0.569 0.397 0.412 0.381 0.566 0.401 0.382 0.568 0.560
## [1957] 0.519 0.530 0.616 0.526 0.560 0.404 0.570 0.640 0.436 0.558 0.630 0.530
## [1969] 0.598 0.556 0.554 0.402 0.450 0.465 0.513 0.433 0.407 0.491 0.473 0.356
## [1981] 0.399 0.450 0.353 0.377 0.379 0.454 0.390 0.406 0.472 0.502 0.362 0.437
## [1993] 0.360 0.357 0.357 0.368 0.358 0.354 0.354 0.358 0.519 0.411 0.612 0.410
## [2005] 0.519 0.411 0.463 0.512 0.586 0.413 0.562 0.559 0.516 0.514 0.419 0.431
## [2017] 0.437 0.446 0.432 0.355 0.593 0.409 0.394 0.592 0.408 0.472 0.382 0.578
## [2029] 0.360 0.436 0.530 0.428 0.372 0.421 0.361 0.376 0.491 0.463 0.369 0.447
## [2041] 0.479 0.409 0.392 0.357 0.412 0.355 0.526 0.436 0.449 0.384 0.363 0.352
## [2053] 0.403 0.403 0.407 0.355 0.369 0.490 0.392 0.415 0.453 0.436 0.490 0.402
## [2065] 0.392 0.403 0.385 0.382 0.681 0.672 0.698 0.599 0.419 0.449 0.404 0.545
## [2077] 0.364 0.562 0.615 0.487 0.568 0.662 0.703 0.572 0.516 0.652 0.873 0.475
## [2089] 0.399 0.690 1.443 0.564 0.509 0.419 0.481 0.612 0.970 0.435 0.599 0.671
## [2101] 0.916 0.675 0.390 0.453 0.792 1.186 0.935 0.726 0.851 0.435 0.629 0.718
## [2113] 1.008 0.454 0.597 0.432 0.373 0.444 0.544 0.653 0.377 0.702 0.398 0.469
## [2125] 0.391 0.442 0.365 0.362 0.601 0.522 0.605 0.541 0.534 0.636 0.449 0.439
## [2137] 0.486 0.431 0.454 0.412 0.396 0.581 0.732 0.614 0.475 0.406 0.591 0.571
## [2149] 0.381 0.490 0.411 0.586 0.382 0.364 0.664 1.527 1.016 0.919 0.768 0.758
## [2161] 0.820 1.663 0.569 0.666 0.421 1.079 0.777 1.020 1.115 0.361 0.565 0.433
## [2173] 0.363 0.381 0.443 0.373 0.560 0.416 0.424 0.429 0.593 0.374 0.606 0.488
## [2185] 0.371 0.577 0.411 0.826 0.951 0.759 0.450 0.668 0.461 0.620 0.584 1.047
## [2197] 1.089 0.895 0.372 0.797 0.392 1.930 0.472 0.502 0.496 0.473 0.443 0.956
## [2209] 1.067 0.772 0.890 0.872 1.188 0.431 0.395 0.538 0.413 0.429 0.371 0.532
## [2221] 0.450 0.511 0.409 0.528 0.371 0.366 0.424 0.486 0.437 0.386 0.500 0.528
## [2233] 0.588 0.421 1.350 0.635 1.623 0.815 0.565 0.395 0.929 0.742 0.458 0.456
## [2245] 0.382 0.358 1.794 0.455 0.544 0.365 1.586 0.453 0.728 1.075 0.937 0.786
## [2257] 0.506 0.659 0.763 0.393 0.372 0.388 0.448 0.482 0.507 0.543 0.535 0.441
## [2269] 0.592 0.671 0.436 0.525 0.401 0.404 0.376 0.374 0.368 0.449 0.432 0.394
## [2281] 0.405 0.360 0.519 0.452 0.516 0.407 0.551 0.373 0.406 0.417 0.409 0.447
## [2293] 0.612 0.355 0.794 0.489 0.916 0.633 0.551 0.590 0.609 0.658 0.635 0.701
## [2305] 0.470 0.379 0.409 0.680 0.956 0.362 0.616 0.396 0.358 0.372 0.386 0.463
## [2317] 0.463 0.497 0.400 0.383 0.354 0.395 0.412 0.493 0.400 0.631 0.358 0.380
## [2329] 1.395 0.820 0.537 0.818 0.924 0.920 0.481 0.584 0.359 0.462 0.438 0.995
## [2341] 1.124 0.430 0.656 0.472 0.395 0.386 0.852 0.385 0.369 0.397 0.413 0.413
## [2353] 0.376 0.480 0.495 0.457 0.507 0.514 0.486 0.425 0.457 0.548 0.587 0.508
## [2365] 0.438 0.591 0.479 0.460 0.408 0.364 0.365 0.517 0.433 0.524 0.411 0.359
## [2377] 0.407 0.360 0.371 0.459 0.447 0.466 0.547 0.424 0.441 0.554 0.361 0.361
## [2389] 0.440 0.389 0.363 0.432 0.369 0.424 0.355 0.379 0.393 0.363 0.401 0.383
## [2401] 0.406 0.382 0.373 0.362 0.475 0.370 0.365 0.361 0.403 0.405 0.413 0.386
## [2413] 0.369 0.352 0.374 0.366 0.382 0.441 0.400 0.355 0.526 0.354 0.390 0.466
## [2425] 0.441 0.364 0.491
rem_out1_kud50 <- subset(week_kuds, KUD50 < 0.351)
boxplot(rem_out1_kud50$KUD50, col = "green2", ylab="KUD50", main="Boxplot of KUD50 after the first outliers removal")

#Second removal
boxplot.stats(rem_out1_kud95$KUD95) #superior limit where outliers start is 1.494
## $stats
## [1] 0.7600 0.7620 0.7935 1.0550 1.4940
## 
## $n
## [1] 23372
## 
## $conf
## [1] 0.7904719 0.7965281
## 
## $out
##    [1] 1.772 1.812 1.736 1.812 1.818 1.800 1.790 1.642 1.577 1.780 1.762 1.762
##   [13] 1.803 1.782 1.819 1.496 1.629 1.738 1.714 1.779 1.697 1.587 1.733 1.755
##   [25] 1.751 1.797 1.726 1.699 1.685 1.774 1.814 1.724 1.799 1.668 1.716 1.673
##   [37] 1.788 1.553 1.659 1.621 1.606 1.702 1.512 1.652 1.552 1.653 1.734 1.679
##   [49] 1.728 1.763 1.623 1.531 1.611 1.516 1.508 1.612 1.508 1.781 1.802 1.597
##   [61] 1.793 1.560 1.751 1.593 1.651 1.648 1.538 1.529 1.502 1.803 1.646 1.820
##   [73] 1.599 1.736 1.510 1.825 1.512 1.623 1.535 1.685 1.732 1.806 1.748 1.584
##   [85] 1.672 1.554 1.784 1.504 1.516 1.815 1.667 1.564 1.578 1.578 1.570 1.509
##   [97] 1.720 1.520 1.606 1.520 1.505 1.495 1.648 1.567 1.639 1.669 1.710 1.537
##  [109] 1.658 1.686 1.537 1.576 1.566 1.650 1.516 1.527 1.541 1.525 1.629 1.797
##  [121] 1.642 1.786 1.735 1.736 1.705 1.679 1.604 1.620 1.654 1.694 1.752 1.825
##  [133] 1.719 1.522 1.710 1.630 1.816 1.535 1.721 1.519 1.703 1.764 1.500 1.501
##  [145] 1.546 1.783 1.807 1.625 1.705 1.687 1.626 1.773 1.752 1.673 1.746 1.788
##  [157] 1.767 1.625 1.672 1.548 1.501 1.507 1.495 1.631 1.620 1.587 1.661 1.777
##  [169] 1.652 1.582 1.653 1.782 1.723 1.561 1.742 1.623 1.732 1.820 1.722 1.820
##  [181] 1.761 1.655 1.696 1.567 1.713 1.585 1.573 1.575 1.509 1.498 1.760 1.794
##  [193] 1.717 1.689 1.608 1.796 1.535 1.620 1.588 1.749 1.532 1.722 1.723 1.550
##  [205] 1.674 1.667 1.710 1.516 1.780 1.617 1.793 1.687 1.499 1.819 1.806 1.700
##  [217] 1.559 1.644 1.657 1.791 1.496 1.601 1.642 1.525 1.681 1.669 1.765 1.781
##  [229] 1.587 1.798 1.740 1.747 1.659 1.516 1.819 1.826 1.748 1.761 1.779 1.619
##  [241] 1.705 1.756 1.517 1.703 1.567 1.601 1.629 1.522 1.561 1.787 1.717 1.820
##  [253] 1.576 1.553 1.534 1.497 1.708 1.787 1.726 1.726 1.640 1.593 1.528 1.527
##  [265] 1.516 1.516 1.517 1.517 1.517 1.516 1.526 1.524 1.714 1.593 1.736 1.513
##  [277] 1.516 1.526 1.516 1.554 1.725 1.558 1.717 1.516 1.524 1.809 1.516 1.496
##  [289] 1.521 1.632 1.517 1.745 1.670 1.699 1.554 1.496 1.534 1.499 1.723 1.822
##  [301] 1.728 1.765 1.651 1.628 1.715 1.742 1.743 1.731 1.593 1.697 1.497 1.525
##  [313] 1.694 1.636 1.517 1.704 1.729 1.716 1.517 1.725 1.694 1.668 1.742 1.501
##  [325] 1.562 1.513 1.652 1.639 1.572 1.517 1.771 1.753 1.679 1.620 1.666 1.674
##  [337] 1.723 1.672 1.742 1.753 1.691 1.645 1.695 1.670 1.514 1.535 1.517 1.532
##  [349] 1.496 1.502 1.737 1.647 1.739 1.632 1.615 1.825 1.803 1.499 1.524 1.508
##  [361] 1.523 1.528 1.519 1.495 1.523 1.526 1.813 1.809 1.691 1.510 1.527 1.524
##  [373] 1.526 1.526 1.513 1.535 1.521 1.527 1.526 1.524 1.525 1.516 1.531 1.527
##  [385] 1.803 1.590 1.740 1.548 1.819 1.644 1.584 1.529 1.729 1.751 1.527 1.619
##  [397] 1.743 1.712 1.679 1.703 1.627 1.526 1.699 1.745 1.512 1.817 1.640 1.592
##  [409] 1.698 1.824 1.676 1.701 1.661 1.578 1.574 1.519 1.548 1.662 1.560 1.520
##  [421] 1.724 1.611 1.502 1.527 1.501 1.541 1.585 1.560 1.559 1.637 1.561 1.569
##  [433] 1.543 1.613 1.625 1.663 1.562 1.523 1.562 1.547 1.693 1.825 1.724 1.726
##  [445] 1.569 1.522 1.587 1.756 1.785 1.813 1.740 1.649 1.610 1.676 1.678 1.719
##  [457] 1.629 1.598 1.743 1.677 1.525 1.686 1.741 1.737 1.805 1.796 1.739 1.504
##  [469] 1.509 1.520 1.509 1.519 1.634 1.531 1.500 1.501 1.524 1.550 1.526 1.497
##  [481] 1.776 1.685 1.683 1.564 1.515 1.518 1.807 1.688 1.566 1.741 1.657 1.535
##  [493] 1.674 1.685 1.667 1.558 1.552 1.496 1.624 1.569 1.579 1.710 1.528 1.755
##  [505] 1.596 1.646 1.583 1.686 1.592 1.670 1.557 1.594 1.560 1.527 1.545 1.512
##  [517] 1.609 1.686 1.551 1.668 1.772 1.520 1.589 1.507 1.566 1.538 1.678 1.690
##  [529] 1.529 1.638 1.543 1.525 1.533 1.519 1.563 1.546 1.562 1.570 1.522 1.748
##  [541] 1.824 1.502 1.690 1.720 1.783 1.638 1.679 1.699 1.722 1.628 1.700 1.762
##  [553] 1.734 1.812 1.631 1.631 1.598 1.568 1.727 1.552 1.684 1.596 1.775 1.707
##  [565] 1.549 1.580 1.769 1.746 1.514 1.751 1.520 1.755 1.826 1.511 1.618 1.767
##  [577] 1.759 1.703 1.585 1.637 1.512 1.706 1.579 1.524 1.739 1.736 1.584 1.523
##  [589] 1.629 1.661 1.664 1.526 1.532 1.554 1.704 1.797 1.561 1.825 1.770 1.779
##  [601] 1.502 1.660 1.635 1.805 1.637 1.738 1.750 1.723 1.581 1.604 1.604 1.504
##  [613] 1.615 1.528 1.636 1.570 1.685 1.785 1.703 1.498 1.641 1.550 1.651 1.645
##  [625] 1.534 1.774 1.581 1.583 1.608 1.755 1.572 1.519 1.695 1.598 1.565 1.741
##  [637] 1.715 1.712 1.667 1.729 1.735 1.604 1.691 1.707 1.642 1.700 1.678 1.539
##  [649] 1.800 1.565 1.732 1.615 1.619 1.503 1.800 1.705 1.754 1.709 1.692 1.778
##  [661] 1.609 1.736 1.516 1.577 1.600 1.731 1.503 1.758 1.596 1.622 1.694 1.645
##  [673] 1.764 1.517 1.558 1.651 1.783 1.780 1.768 1.779 1.662 1.673 1.808 1.760
##  [685] 1.657 1.531 1.573 1.574 1.554 1.553 1.658 1.675 1.503 1.546 1.507 1.619
##  [697] 1.557 1.791 1.741 1.663 1.628 1.555 1.612 1.739 1.623 1.703 1.712 1.543
##  [709] 1.549 1.628 1.675 1.580 1.542 1.569 1.518 1.602 1.591 1.521 1.604 1.587
##  [721] 1.510 1.633 1.502 1.512 1.504 1.686 1.682 1.521 1.726 1.612 1.706 1.812
##  [733] 1.614 1.599 1.551 1.584 1.752 1.672 1.676 1.507 1.644 1.736 1.498 1.633
##  [745] 1.496 1.524 1.524 1.507 1.551 1.810 1.601 1.515 1.567 1.625 1.600 1.684
##  [757] 1.581 1.515 1.532 1.641 1.496 1.553 1.571 1.792 1.518 1.527 1.648 1.545
##  [769] 1.783 1.646 1.808 1.656 1.788 1.525 1.503 1.767 1.730 1.504 1.705 1.506
##  [781] 1.799 1.729 1.531 1.673 1.595 1.541 1.741 1.519 1.541 1.816 1.739 1.571
##  [793] 1.594 1.756 1.496 1.564 1.549 1.688 1.511 1.663 1.778 1.721 1.523 1.679
##  [805] 1.662 1.771 1.509 1.662 1.672 1.672 1.808 1.512 1.540 1.594 1.546 1.516
##  [817] 1.514 1.820 1.496 1.517 1.522 1.809 1.525 1.826 1.520 1.507 1.821 1.815
##  [829] 1.786 1.668 1.704 1.563 1.728 1.534 1.680 1.633 1.617 1.605 1.666 1.499
##  [841] 1.594 1.793 1.590 1.523 1.608 1.497 1.640 1.539 1.519 1.528 1.517 1.530
##  [853] 1.615 1.528 1.641 1.505 1.534 1.530 1.575 1.530 1.575 1.634 1.498 1.742
##  [865] 1.520 1.721 1.496 1.496 1.522 1.496 1.618 1.623 1.564 1.655 1.587 1.525
##  [877] 1.516 1.496 1.676 1.687 1.574 1.626 1.785 1.675 1.655 1.518 1.510 1.536
##  [889] 1.808 1.552 1.554 1.537 1.556 1.563 1.566 1.592 1.610 1.536 1.600 1.575
##  [901] 1.506 1.537 1.556 1.585 1.554 1.574 1.600 1.556 1.568 1.809 1.584 1.673
##  [913] 1.747 1.761 1.754 1.794 1.721 1.713 1.635 1.628 1.606 1.714 1.623 1.638
##  [925] 1.514 1.811 1.713 1.646 1.539 1.762 1.744 1.754 1.668 1.551 1.701 1.809
##  [937] 1.639 1.820 1.738 1.540 1.628 1.777 1.792 1.533 1.588 1.587 1.523 1.528
##  [949] 1.628 1.572 1.774 1.702 1.660 1.694 1.693 1.635 1.605 1.684 1.823 1.601
##  [961] 1.809 1.593 1.706 1.542 1.738 1.720 1.798 1.648 1.723 1.631 1.519 1.666
##  [973] 1.704 1.587 1.763 1.564 1.658 1.710 1.633 1.804 1.740 1.574 1.634 1.540
##  [985] 1.501 1.716 1.639 1.520 1.525 1.517 1.516 1.512 1.528 1.527 1.527 1.524
##  [997] 1.522 1.524 1.510 1.636 1.507 1.516 1.505 1.517 1.524 1.513 1.523 1.500
## [1009] 1.523 1.517 1.806 1.497 1.527 1.517 1.517 1.509 1.518 1.751 1.517 1.606
## [1021] 1.663 1.528 1.754 1.527 1.802 1.528 1.518 1.774 1.526 1.756 1.501 1.734
## [1033] 1.526 1.509 1.527 1.552 1.807 1.518 1.500 1.506 1.499 1.781 1.524 1.506
## [1045] 1.527 1.522 1.510 1.522 1.810 1.526 1.524 1.602 1.523 1.519 1.518 1.505
## [1057] 1.532 1.527 1.516 1.522 1.523 1.510 1.528 1.511 1.496 1.726 1.521 1.517
## [1069] 1.496 1.507 1.517 1.763 1.680 1.637 1.527 1.667 1.507 1.795 1.526 1.744
## [1081] 1.522 1.497 1.521 1.509 1.506 1.526 1.507 1.810 1.518 1.497 1.518 1.527
## [1093] 1.520 1.497 1.528 1.508 1.668 1.527 1.510 1.517 1.517 1.764 1.525 1.523
## [1105] 1.527 1.517 1.526 1.593 1.522 1.527 1.802 1.527 1.524 1.646 1.524 1.812
## [1117] 1.528 1.528 1.518 1.528 1.503 1.521 1.767 1.519 1.508 1.520 1.522 1.519
## [1129] 1.505 1.769 1.523 1.516 1.517 1.528 1.507 1.522 1.517 1.517 1.520 1.527
## [1141] 1.709 1.655 1.506 1.758 1.683 1.523 1.520 1.524 1.517 1.528 1.525 1.509
## [1153] 1.526 1.500 1.505 1.504 1.519 1.497 1.510 1.505 1.509 1.528 1.523 1.516
## [1165] 1.524 1.626 1.527 1.763 1.525 1.503 1.553 1.516 1.523 1.522 1.634 1.512
## [1177] 1.506 1.527 1.508 1.510 1.523 1.526 1.516 1.497 1.524 1.496 1.814 1.517
## [1189] 1.513 1.527 1.502 1.497 1.504 1.511 1.629 1.526 1.503 1.524 1.521 1.523
## [1201] 1.790 1.589 1.600 1.514 1.560 1.740 1.594 1.713 1.640 1.765 1.507 1.689
## [1213] 1.519 1.637 1.819 1.678 1.626 1.747 1.641 1.693 1.496 1.798 1.639 1.545
## [1225] 1.652 1.609 1.556 1.769 1.731 1.570 1.802 1.775 1.499 1.695 1.497 1.708
## [1237] 1.584 1.815 1.629 1.667 1.585 1.816 1.570 1.525 1.626 1.655 1.620 1.811
## [1249] 1.625 1.804 1.567 1.767 1.631 1.812 1.520 1.548 1.594 1.729 1.560 1.630
## [1261] 1.725 1.722 1.785 1.675 1.632 1.700 1.527 1.585 1.610 1.506 1.701 1.524
## [1273] 1.704 1.817 1.688 1.554 1.625 1.677 1.819 1.663 1.644 1.790 1.604 1.638
## [1285] 1.543 1.544 1.625 1.739 1.572 1.662 1.701 1.528 1.615 1.543 1.584 1.551
## [1297] 1.684 1.692 1.736 1.662 1.645 1.695 1.676 1.681 1.637 1.789 1.564 1.743
## [1309] 1.635 1.800 1.553 1.822 1.583 1.768 1.560 1.563 1.551 1.513 1.532 1.584
## [1321] 1.751 1.757 1.726 1.823 1.651 1.603 1.608 1.625 1.771 1.515 1.768 1.548
## [1333] 1.617 1.496 1.640 1.784 1.652 1.721 1.683 1.782 1.526 1.714 1.628 1.795
## [1345] 1.662 1.590 1.586 1.580 1.597 1.495 1.572 1.510 1.582 1.712 1.656 1.657
## [1357] 1.515 1.556 1.714 1.505 1.527 1.526 1.527 1.518 1.785 1.515 1.526 1.604
## [1369] 1.515 1.508 1.525 1.514 1.522 1.790 1.753 1.593 1.527 1.523 1.504 1.500
## [1381] 1.520 1.708 1.503 1.528 1.603 1.518 1.527 1.506 1.524 1.686 1.632 1.526
## [1393] 1.746 1.527 1.768 1.553 1.521 1.505 1.527 1.515 1.511 1.514 1.517 1.513
## [1405] 1.517 1.524 1.520 1.514 1.527 1.501 1.526 1.657 1.507 1.525 1.524 1.527
## [1417] 1.517 1.511 1.524 1.740 1.507 1.527 1.527 1.528 1.507 1.523 1.619 1.511
## [1429] 1.503 1.503 1.619 1.504 1.655 1.509 1.526 1.518 1.511 1.805 1.523 1.796
## [1441] 1.527 1.499 1.527 1.502 1.517 1.496 1.507 1.527 1.517 1.522 1.598 1.572
## [1453] 1.710 1.660 1.581 1.712 1.674 1.622 1.702 1.607 1.791 1.779 1.826 1.781
## [1465] 1.675 1.803 1.579 1.742 1.600 1.763 1.686 1.676 1.662 1.551 1.681 1.698
## [1477] 1.654 1.674 1.579 1.766 1.583 1.634 1.715 1.687 1.610 1.697 1.640 1.684
## [1489] 1.587 1.788 1.566 1.571 1.613 1.715 1.607 1.521 1.719 1.498 1.707 1.541
## [1501] 1.601 1.701 1.572 1.648 1.684 1.658 1.518 1.780 1.826 1.685 1.751 1.654
## [1513] 1.512 1.752 1.773 1.786 1.599 1.624 1.723 1.520 1.684 1.723 1.554 1.750
## [1525] 1.504 1.524 1.726 1.779 1.731 1.655 1.615 1.801 1.654 1.798 1.646 1.501
## [1537] 1.555 1.652 1.716 1.694 1.772 1.758 1.767 1.740 1.548 1.649 1.594 1.738
## [1549] 1.548 1.601 1.807 1.784 1.503 1.776 1.793 1.531 1.539 1.527 1.523 1.519
## [1561] 1.512 1.623 1.818 1.675 1.711 1.637 1.556 1.498 1.503 1.783 1.700 1.652
## [1573] 1.569 1.600 1.640 1.518 1.706 1.566 1.688 1.729
rem_out2_kud95 <- subset(week_kuds, KUD95 < 1.494)
boxplot(rem_out2_kud95$KUD95, col = "deepskyblue", ylab="KUD95", main="Boxplot of KUD95 after the second outliers removal")

boxplot.stats(rem_out1_kud50$KUD50) #superior limit where outliers start is 0.281
## $stats
## [1] 0.162 0.164 0.169 0.211 0.281
## 
## $n
## [1] 23164
## 
## $conf
## [1] 0.1685121 0.1694879
## 
## $out
##    [1] 0.340 0.295 0.308 0.345 0.309 0.334 0.326 0.347 0.349 0.294 0.290 0.300
##   [13] 0.287 0.289 0.288 0.293 0.311 0.317 0.340 0.284 0.340 0.337 0.283 0.285
##   [25] 0.302 0.339 0.286 0.292 0.325 0.330 0.290 0.348 0.290 0.291 0.337 0.343
##   [37] 0.301 0.344 0.321 0.329 0.314 0.314 0.341 0.349 0.350 0.307 0.311 0.303
##   [49] 0.299 0.291 0.314 0.350 0.292 0.287 0.319 0.287 0.282 0.316 0.293 0.293
##   [61] 0.298 0.302 0.323 0.315 0.282 0.332 0.312 0.299 0.334 0.292 0.321 0.338
##   [73] 0.332 0.307 0.316 0.300 0.309 0.289 0.319 0.295 0.307 0.326 0.288 0.289
##   [85] 0.285 0.305 0.283 0.282 0.283 0.286 0.316 0.293 0.341 0.282 0.289 0.308
##   [97] 0.307 0.292 0.295 0.295 0.337 0.330 0.344 0.298 0.334 0.287 0.333 0.289
##  [109] 0.330 0.350 0.338 0.326 0.285 0.302 0.349 0.286 0.299 0.297 0.296 0.293
##  [121] 0.282 0.318 0.324 0.318 0.286 0.330 0.321 0.307 0.337 0.291 0.290 0.345
##  [133] 0.283 0.284 0.297 0.285 0.300 0.308 0.300 0.338 0.330 0.305 0.324 0.333
##  [145] 0.311 0.304 0.297 0.331 0.292 0.337 0.314 0.348 0.292 0.307 0.320 0.283
##  [157] 0.331 0.316 0.337 0.312 0.328 0.338 0.323 0.329 0.328 0.328 0.298 0.303
##  [169] 0.325 0.311 0.339 0.338 0.344 0.347 0.303 0.298 0.293 0.344 0.334 0.294
##  [181] 0.346 0.336 0.346 0.285 0.283 0.319 0.332 0.326 0.306 0.314 0.291 0.334
##  [193] 0.309 0.349 0.302 0.283 0.311 0.294 0.287 0.346 0.336 0.287 0.297 0.350
##  [205] 0.290 0.305 0.345 0.327 0.310 0.285 0.294 0.334 0.329 0.292 0.328 0.301
##  [217] 0.329 0.325 0.301 0.283 0.284 0.344 0.317 0.320 0.316 0.326 0.305 0.294
##  [229] 0.330 0.332 0.333 0.322 0.323 0.323 0.322 0.323 0.322 0.332 0.330 0.307
##  [241] 0.319 0.322 0.331 0.322 0.302 0.331 0.343 0.322 0.300 0.329 0.327 0.333
##  [253] 0.322 0.295 0.285 0.303 0.301 0.305 0.315 0.329 0.339 0.294 0.318 0.322
##  [265] 0.319 0.349 0.331 0.304 0.332 0.337 0.283 0.322 0.346 0.322 0.286 0.343
##  [277] 0.319 0.293 0.322 0.295 0.297 0.306 0.329 0.316 0.328 0.299 0.339 0.345
##  [289] 0.297 0.322 0.330 0.324 0.329 0.300 0.307 0.319 0.312 0.309 0.318 0.327
##  [301] 0.301 0.329 0.313 0.330 0.331 0.326 0.299 0.331 0.330 0.293 0.284 0.343
##  [313] 0.291 0.348 0.333 0.328 0.332 0.333 0.320 0.288 0.330 0.326 0.333 0.330
##  [325] 0.330 0.332 0.323 0.329 0.331 0.319 0.331 0.349 0.294 0.331 0.288 0.347
##  [337] 0.297 0.308 0.306 0.304 0.337 0.343 0.337 0.295 0.322 0.343 0.347 0.319
##  [349] 0.285 0.343 0.283 0.308 0.325 0.310 0.345 0.314 0.287 0.283 0.321 0.310
##  [361] 0.316 0.338 0.345 0.287 0.330 0.349 0.311 0.296 0.298 0.283 0.293 0.285
##  [373] 0.331 0.342 0.339 0.330 0.344 0.303 0.338 0.340 0.340 0.284 0.350 0.321
##  [385] 0.312 0.327 0.297 0.327 0.327 0.292 0.295 0.315 0.337 0.338 0.337 0.350
##  [397] 0.304 0.313 0.288 0.348 0.326 0.292 0.332 0.320 0.311 0.333 0.345 0.345
##  [409] 0.342 0.304 0.310 0.328 0.337 0.301 0.312 0.334 0.316 0.320 0.333 0.322
##  [421] 0.296 0.329 0.294 0.291 0.314 0.328 0.325 0.341 0.308 0.282 0.325 0.328
##  [433] 0.290 0.285 0.310 0.340 0.348 0.284 0.283 0.302 0.302 0.305 0.335 0.325
##  [445] 0.311 0.287 0.322 0.298 0.336 0.316 0.325 0.325 0.344 0.334 0.306 0.290
##  [457] 0.296 0.316 0.304 0.295 0.332 0.325 0.302 0.348 0.325 0.329 0.323 0.321
##  [469] 0.289 0.323 0.336 0.332 0.321 0.300 0.335 0.325 0.305 0.311 0.313 0.288
##  [481] 0.287 0.299 0.330 0.347 0.334 0.323 0.303 0.316 0.287 0.304 0.313 0.293
##  [493] 0.296 0.294 0.316 0.298 0.305 0.282 0.334 0.307 0.332 0.285 0.332 0.288
##  [505] 0.327 0.325 0.290 0.336 0.340 0.335 0.343 0.287 0.339 0.317 0.336 0.334
##  [517] 0.290 0.283 0.291 0.298 0.290 0.349 0.338 0.300 0.337 0.332 0.318 0.308
##  [529] 0.290 0.282 0.284 0.283 0.302 0.283 0.290 0.298 0.282 0.319 0.302 0.290
##  [541] 0.342 0.334 0.341 0.344 0.302 0.320 0.315 0.296 0.290 0.291 0.314 0.319
##  [553] 0.324 0.294 0.320 0.343 0.319 0.341 0.317 0.314 0.342 0.337 0.344 0.318
##  [565] 0.294 0.322 0.314 0.335 0.308 0.317 0.294 0.300 0.283 0.301 0.291 0.325
##  [577] 0.319 0.330 0.333 0.311 0.338 0.339 0.314 0.333 0.282 0.299 0.304 0.286
##  [589] 0.282 0.285 0.282 0.317 0.289 0.342 0.326 0.308 0.330 0.336 0.336 0.318
##  [601] 0.282 0.311 0.333 0.327 0.303 0.344 0.330 0.330 0.299 0.325 0.310 0.340
##  [613] 0.307 0.341 0.313 0.298 0.344 0.328 0.338 0.297 0.289 0.297 0.285 0.300
##  [625] 0.289 0.350 0.328 0.298 0.350 0.298 0.330 0.329 0.331 0.318 0.316 0.295
##  [637] 0.311 0.327 0.302 0.295 0.289 0.305 0.312 0.284 0.289 0.300 0.322 0.318
##  [649] 0.303 0.292 0.303 0.295 0.311 0.347 0.283 0.310 0.311 0.325 0.314 0.307
##  [661] 0.311 0.288 0.343 0.330 0.294 0.310 0.320 0.307 0.309 0.336 0.282 0.308
##  [673] 0.303 0.300 0.298 0.296 0.299 0.319 0.308 0.309 0.323 0.292 0.347 0.287
##  [685] 0.321 0.295 0.344 0.328 0.292 0.286 0.313 0.340 0.343 0.284 0.321 0.292
##  [697] 0.331 0.349 0.294 0.284 0.283 0.298 0.282 0.325 0.331 0.345 0.283 0.282
##  [709] 0.304 0.291 0.314 0.337 0.304 0.302 0.324 0.287 0.282 0.285 0.290 0.284
##  [721] 0.330 0.337 0.298 0.323 0.306 0.303 0.320 0.294 0.304 0.306 0.302 0.320
##  [733] 0.286 0.294 0.314 0.289 0.297 0.310 0.309 0.300 0.312 0.305 0.316 0.324
##  [745] 0.292 0.289 0.311 0.283 0.296 0.302 0.304 0.288 0.337 0.340 0.302 0.328
##  [757] 0.333 0.292 0.314 0.305 0.309 0.326 0.321 0.304 0.333 0.297 0.292 0.319
##  [769] 0.314 0.287 0.289 0.288 0.329 0.317 0.321 0.299 0.307 0.290 0.330 0.284
##  [781] 0.296 0.296 0.305 0.323 0.286 0.283 0.285 0.286 0.282 0.286 0.307 0.293
##  [793] 0.303 0.320 0.322 0.290 0.285 0.314 0.344 0.293 0.326 0.337 0.337 0.286
##  [805] 0.328 0.285 0.319 0.293 0.334 0.289 0.302 0.290 0.288 0.302 0.286 0.297
##  [817] 0.282 0.296 0.316 0.299 0.343 0.327 0.317 0.296 0.296 0.301 0.331 0.287
##  [829] 0.322 0.303 0.283 0.284 0.301 0.317 0.299 0.285 0.313 0.310 0.327 0.332
##  [841] 0.321 0.322 0.292 0.333 0.320 0.338 0.325 0.345 0.327 0.294 0.307 0.314
##  [853] 0.323 0.343 0.310 0.306 0.324 0.284 0.323 0.326 0.347 0.339 0.325 0.317
##  [865] 0.301 0.336 0.339 0.313 0.297 0.322 0.340 0.316 0.321 0.343 0.336 0.342
##  [877] 0.297 0.285 0.288 0.305 0.326 0.324 0.301 0.305 0.285 0.300 0.315 0.296
##  [889] 0.288 0.298 0.298 0.319 0.318 0.324 0.333 0.303 0.289 0.308 0.289 0.290
##  [901] 0.331 0.341 0.319 0.295 0.296 0.312 0.338 0.332 0.309 0.296 0.286 0.292
##  [913] 0.296 0.309 0.323 0.334 0.282 0.283 0.335 0.331 0.292 0.319 0.283 0.340
##  [925] 0.287 0.283 0.306 0.328 0.303 0.327 0.331 0.291 0.335 0.325 0.333 0.302
##  [937] 0.327 0.317 0.337 0.323 0.318 0.305 0.288 0.315 0.293 0.286 0.286 0.328
##  [949] 0.325 0.298 0.350 0.283 0.328 0.320 0.326 0.285 0.335 0.309 0.305 0.340
##  [961] 0.331 0.321 0.338 0.325 0.311 0.293 0.332 0.288 0.293 0.298 0.284 0.314
##  [973] 0.345 0.285 0.339 0.350 0.301 0.303 0.286 0.333 0.326 0.315 0.327 0.327
##  [985] 0.325 0.345 0.299 0.327 0.329 0.335 0.297 0.312 0.298 0.330 0.346 0.293
##  [997] 0.342 0.296 0.314 0.300 0.347 0.317 0.307 0.282 0.320 0.311 0.289 0.326
## [1009] 0.319 0.333 0.294 0.341 0.286 0.283 0.283 0.321 0.299 0.330 0.330 0.320
## [1021] 0.313 0.315 0.331 0.316 0.287 0.328 0.315 0.318 0.347 0.312 0.334 0.297
## [1033] 0.333 0.307 0.315 0.289 0.306 0.291 0.297 0.303 0.350 0.296 0.304 0.294
## [1045] 0.303 0.282 0.296 0.289 0.313 0.293 0.282 0.337 0.321 0.306 0.334 0.299
## [1057] 0.282 0.325 0.291 0.323 0.332 0.289 0.332 0.314 0.302 0.316 0.323 0.314
## [1069] 0.339 0.302 0.283 0.347 0.333 0.348 0.324 0.306 0.291 0.332 0.343 0.332
## [1081] 0.340 0.283 0.302 0.315 0.343 0.300 0.286 0.284 0.284 0.317 0.293 0.290
## [1093] 0.303 0.316 0.288 0.321 0.342 0.299 0.346 0.310 0.328 0.300 0.347 0.306
## [1105] 0.347 0.308 0.295 0.287 0.282 0.286 0.293 0.282 0.290 0.308 0.289 0.323
## [1117] 0.326 0.300 0.310 0.294 0.291 0.300 0.337 0.323 0.291 0.326 0.317 0.293
## [1129] 0.295 0.328 0.303 0.286 0.325 0.343 0.328 0.287 0.299 0.284 0.301 0.348
## [1141] 0.303 0.318 0.316 0.311 0.333 0.328 0.305 0.308 0.349 0.324 0.341 0.342
## [1153] 0.288 0.288 0.294 0.330 0.312 0.289 0.326 0.308 0.339 0.341 0.329 0.298
## [1165] 0.320 0.300 0.307 0.292 0.323 0.312 0.308 0.343 0.326 0.286 0.299 0.299
## [1177] 0.328 0.299 0.301 0.314 0.283 0.283 0.330 0.309 0.322 0.299 0.306 0.286
## [1189] 0.342 0.291 0.308 0.302 0.311 0.285 0.287 0.324 0.284 0.283 0.293 0.335
## [1201] 0.338 0.314 0.337 0.324 0.288 0.329 0.301 0.316 0.292 0.287 0.308 0.334
## [1213] 0.322 0.297 0.285 0.343 0.287 0.329 0.283 0.297 0.309 0.302 0.283 0.309
## [1225] 0.313 0.303 0.324 0.307 0.332 0.316 0.333 0.305 0.330 0.303 0.319 0.324
## [1237] 0.342 0.341 0.297 0.314 0.315 0.347 0.300 0.330 0.301 0.284 0.340 0.345
## [1249] 0.350 0.294 0.291 0.300 0.287 0.306 0.301 0.284 0.312 0.340 0.290 0.326
## [1261] 0.330 0.294 0.322 0.321 0.318 0.332 0.332 0.333 0.329 0.327 0.329 0.316
## [1273] 0.333 0.295 0.314 0.322 0.311 0.322 0.329 0.319 0.330 0.329 0.323 0.304
## [1285] 0.291 0.331 0.343 0.322 0.322 0.316 0.324 0.314 0.288 0.332 0.321 0.331
## [1297] 0.282 0.292 0.306 0.327 0.332 0.324 0.329 0.330 0.319 0.306 0.330 0.286
## [1309] 0.331 0.294 0.289 0.326 0.306 0.312 0.335 0.305 0.329 0.296 0.299 0.306
## [1321] 0.312 0.317 0.288 0.323 0.331 0.283 0.327 0.344 0.289 0.318 0.327 0.331
## [1333] 0.331 0.287 0.327 0.325 0.348 0.324 0.311 0.349 0.331 0.348 0.321 0.327
## [1345] 0.330 0.318 0.320 0.332 0.317 0.337 0.294 0.296 0.327 0.322 0.301 0.313
## [1357] 0.323 0.335 0.295 0.332 0.283 0.313 0.345 0.333 0.329 0.323 0.328 0.304
## [1369] 0.326 0.316 0.313 0.350 0.330 0.314 0.344 0.324 0.304 0.324 0.332 0.325
## [1381] 0.302 0.332 0.315 0.339 0.331 0.318 0.323 0.323 0.330 0.329 0.331 0.322
## [1393] 0.316 0.331 0.327 0.332 0.331 0.329 0.329 0.326 0.294 0.332 0.288 0.332
## [1405] 0.324 0.332 0.309 0.326 0.324 0.341 0.282 0.315 0.326 0.328 0.326 0.311
## [1417] 0.328 0.321 0.298 0.298 0.323 0.332 0.313 0.327 0.323 0.323 0.348 0.326
## [1429] 0.331 0.341 0.328 0.311 0.291 0.298 0.329 0.327 0.289 0.329 0.323 0.332
## [1441] 0.330 0.316 0.331 0.306 0.310 0.310 0.316 0.325 0.304 0.318 0.311 0.316
## [1453] 0.332 0.328 0.323 0.340 0.331 0.331 0.287 0.304 0.333 0.309 0.323 0.328
## [1465] 0.290 0.328 0.284 0.317 0.296 0.312 0.295 0.332 0.315 0.317 0.328 0.331
## [1477] 0.323 0.302 0.330 0.300 0.286 0.323 0.292 0.319 0.332 0.284 0.307 0.304
## [1489] 0.310 0.318 0.331 0.309 0.329 0.328 0.328 0.330 0.304 0.303 0.346 0.345
## [1501] 0.305 0.295 0.297 0.314 0.339 0.320 0.314 0.319 0.328 0.326 0.303 0.311
## [1513] 0.298 0.320 0.344 0.300 0.344 0.332 0.323 0.287 0.284 0.286 0.321 0.294
## [1525] 0.301 0.282 0.339 0.299 0.291 0.306 0.290 0.291 0.287 0.314 0.285 0.328
## [1537] 0.330 0.349 0.282 0.325 0.345 0.315 0.313 0.320 0.315 0.286 0.289 0.315
## [1549] 0.296 0.334 0.330 0.337 0.321 0.329 0.293 0.328 0.321 0.333 0.309 0.312
## [1561] 0.303 0.337 0.304 0.347 0.310 0.324 0.283 0.340 0.324 0.300 0.344 0.334
## [1573] 0.331 0.327 0.331 0.300 0.348 0.317 0.347 0.325 0.296 0.327 0.341 0.338
## [1585] 0.321 0.326 0.297 0.319 0.327 0.322 0.323 0.350 0.349 0.300 0.288 0.344
## [1597] 0.287 0.302 0.317 0.327 0.333 0.283 0.338 0.304 0.296 0.306 0.310 0.294
## [1609] 0.338 0.327 0.329 0.305 0.321 0.313 0.288 0.336 0.285 0.291 0.284 0.307
## [1621] 0.291 0.287 0.326 0.286 0.350 0.342 0.300 0.328 0.312 0.335 0.286 0.291
## [1633] 0.317 0.304 0.331 0.327 0.320 0.323 0.312 0.319 0.339 0.337 0.346 0.340
## [1645] 0.346 0.326 0.344 0.326 0.350 0.323 0.336 0.307 0.285 0.344 0.337 0.316
## [1657] 0.295 0.318 0.290 0.302 0.313 0.292 0.290 0.292 0.296 0.331 0.340 0.285
## [1669] 0.299 0.347 0.299 0.305 0.338 0.299 0.347 0.316 0.315 0.338 0.334 0.333
## [1681] 0.348 0.333 0.339 0.331 0.318 0.327 0.331 0.335 0.284 0.303 0.346 0.349
## [1693] 0.297 0.322 0.317 0.295 0.316 0.293 0.311 0.333 0.332 0.333 0.340 0.324
## [1705] 0.305 0.320 0.296 0.331 0.340 0.321 0.313 0.329 0.320 0.329 0.344 0.305
## [1717] 0.332 0.330 0.310 0.304 0.327 0.311 0.308 0.282 0.332 0.313 0.323 0.331
## [1729] 0.312 0.330 0.331 0.333 0.334 0.323 0.329 0.310 0.332 0.321 0.317 0.320
## [1741] 0.295 0.322 0.296 0.319 0.323 0.332 0.320 0.332 0.307 0.293 0.332 0.305
## [1753] 0.313 0.330 0.284 0.331 0.315 0.333 0.323 0.316 0.303 0.330 0.313 0.333
## [1765] 0.331 0.332 0.313 0.330 0.317 0.308 0.309 0.309 0.337 0.331 0.324 0.301
## [1777] 0.316 0.341 0.331 0.345 0.333 0.303 0.332 0.309 0.322 0.336 0.301 0.312
## [1789] 0.332 0.323 0.331 0.327 0.290 0.333 0.289 0.290 0.293 0.304 0.288 0.318
## [1801] 0.331 0.302 0.314 0.311 0.331 0.302 0.300 0.316 0.321 0.289 0.291 0.332
## [1813] 0.298 0.347 0.297 0.296 0.294 0.298 0.327 0.332 0.345 0.340 0.336 0.284
## [1825] 0.283 0.340 0.330 0.300 0.285 0.290 0.284 0.284 0.329 0.338 0.319 0.304
## [1837] 0.307 0.300 0.306 0.310 0.325 0.350 0.297 0.284 0.331 0.297 0.316 0.299
## [1849] 0.291 0.341 0.308 0.313 0.350 0.306 0.340 0.300 0.340 0.337 0.282 0.350
## [1861] 0.326 0.282 0.332 0.284 0.296 0.287 0.347 0.307 0.293 0.315 0.308 0.342
## [1873] 0.310 0.340 0.294 0.321 0.293 0.341 0.297 0.350 0.314 0.288 0.346 0.298
## [1885] 0.327 0.334 0.290 0.303 0.304 0.293 0.333 0.287 0.349 0.316 0.309 0.319
## [1897] 0.333 0.320 0.305 0.285 0.319 0.337 0.285 0.332 0.299 0.287 0.335 0.328
## [1909] 0.349 0.347 0.306 0.329 0.285 0.341 0.342 0.316 0.316 0.317 0.314 0.325
## [1921] 0.306 0.306 0.322 0.316 0.305 0.311 0.297 0.305 0.340 0.328
rem_out2_kud50 <- subset(week_kuds, KUD50 < 0.281)
boxplot(rem_out2_kud50$KUD50, col = "green2", ylab="KUD50", main="Boxplot of KUD50 after the second outliers removal")

#Third removal
boxplot.stats(rem_out2_kud95$KUD95) #superior limit where outliers start is 1.311
## $stats
## [1] 0.760 0.762 0.779 0.982 1.311
## 
## $n
## [1] 21788
## 
## $conf
## [1] 0.7766451 0.7813549
## 
## $out
##    [1] 1.385 1.433 1.362 1.383 1.454 1.469 1.427 1.430 1.373 1.349 1.478 1.334
##   [13] 1.366 1.390 1.457 1.487 1.386 1.435 1.373 1.389 1.386 1.349 1.405 1.356
##   [25] 1.323 1.403 1.488 1.453 1.423 1.325 1.407 1.392 1.353 1.328 1.354 1.332
##   [37] 1.459 1.471 1.374 1.341 1.360 1.340 1.358 1.338 1.318 1.476 1.362 1.350
##   [49] 1.378 1.391 1.432 1.491 1.356 1.359 1.358 1.316 1.341 1.413 1.431 1.420
##   [61] 1.486 1.411 1.341 1.353 1.327 1.322 1.491 1.442 1.459 1.314 1.364 1.457
##   [73] 1.441 1.413 1.405 1.448 1.327 1.469 1.413 1.428 1.462 1.422 1.468 1.380
##   [85] 1.330 1.384 1.464 1.350 1.422 1.402 1.336 1.341 1.394 1.391 1.467 1.423
##   [97] 1.448 1.339 1.381 1.462 1.414 1.419 1.463 1.355 1.385 1.348 1.415 1.320
##  [109] 1.348 1.335 1.352 1.335 1.326 1.312 1.350 1.358 1.334 1.336 1.351 1.321
##  [121] 1.394 1.312 1.335 1.456 1.492 1.415 1.459 1.397 1.344 1.328 1.328 1.445
##  [133] 1.442 1.429 1.352 1.472 1.353 1.451 1.456 1.481 1.389 1.415 1.334 1.365
##  [145] 1.361 1.421 1.368 1.387 1.425 1.353 1.477 1.329 1.489 1.318 1.363 1.384
##  [157] 1.312 1.322 1.372 1.388 1.413 1.356 1.375 1.355 1.415 1.454 1.333 1.325
##  [169] 1.411 1.375 1.452 1.376 1.368 1.468 1.431 1.345 1.414 1.414 1.345 1.488
##  [181] 1.333 1.439 1.411 1.387 1.411 1.464 1.485 1.453 1.340 1.376 1.313 1.374
##  [193] 1.420 1.319 1.398 1.482 1.411 1.475 1.378 1.325 1.482 1.357 1.483 1.411
##  [205] 1.476 1.342 1.466 1.411 1.387 1.475 1.453 1.400 1.493 1.480 1.454 1.370
##  [217] 1.388 1.442 1.445 1.381 1.461 1.400 1.326 1.423 1.473 1.463 1.339 1.477
##  [229] 1.475 1.447 1.353 1.469 1.313 1.412 1.406 1.457 1.354 1.477 1.476 1.457
##  [241] 1.461 1.386 1.457 1.339 1.490 1.411 1.488 1.493 1.427 1.383 1.338 1.458
##  [253] 1.386 1.339 1.457 1.363 1.487 1.413 1.474 1.368 1.324 1.358 1.456 1.369
##  [265] 1.470 1.453 1.474 1.314 1.381 1.401 1.313 1.344 1.455 1.374 1.455 1.440
##  [277] 1.345 1.486 1.465 1.343 1.315 1.394 1.424 1.379 1.472 1.493 1.473 1.336
##  [289] 1.393 1.403 1.314 1.357 1.440 1.386 1.464 1.430 1.359 1.417 1.378 1.461
##  [301] 1.489 1.407 1.481 1.451 1.424 1.491 1.360 1.359 1.343 1.333 1.409 1.492
##  [313] 1.377 1.409 1.350 1.346 1.337 1.468 1.362 1.361 1.453 1.482 1.435 1.387
##  [325] 1.437 1.368 1.369 1.425 1.470 1.436 1.354 1.397 1.352 1.447 1.395 1.458
##  [337] 1.486 1.433 1.397 1.408 1.474 1.396 1.316 1.391 1.322 1.473 1.407 1.353
##  [349] 1.492 1.313 1.475 1.430 1.454 1.361 1.390 1.456 1.493 1.376 1.316 1.336
##  [361] 1.490 1.349 1.312 1.335 1.410 1.480 1.361 1.433 1.413 1.450 1.350 1.475
##  [373] 1.487 1.364 1.403 1.368 1.347 1.391 1.379 1.385 1.417 1.367 1.387 1.347
##  [385] 1.389 1.398 1.397 1.320 1.341 1.460 1.432 1.328 1.358 1.386 1.368 1.380
##  [397] 1.406 1.326 1.436 1.456 1.445 1.319 1.332 1.341 1.322 1.345 1.424 1.436
##  [409] 1.334 1.367 1.422 1.459 1.367 1.421 1.459 1.431 1.332 1.358 1.328 1.320
##  [421] 1.435 1.434 1.347 1.436 1.322 1.411 1.314 1.403 1.365 1.315 1.323 1.335
##  [433] 1.354 1.378 1.386 1.319 1.323 1.396 1.347 1.452 1.379 1.415 1.432 1.386
##  [445] 1.427 1.363 1.466 1.417 1.354 1.467 1.396 1.321 1.413 1.356 1.424 1.444
##  [457] 1.337 1.482 1.410 1.319 1.435 1.365 1.353 1.349 1.354 1.327 1.400 1.374
##  [469] 1.333 1.408 1.327 1.452 1.493 1.488 1.374 1.313 1.321 1.398 1.381 1.333
##  [481] 1.386 1.486 1.412 1.323 1.331 1.326 1.355 1.397 1.381 1.393 1.329 1.486
##  [493] 1.329 1.372 1.369 1.348 1.316 1.353 1.383 1.435 1.320 1.398 1.348 1.336
##  [505] 1.404 1.483 1.478 1.313 1.368 1.447 1.470 1.321 1.316 1.340 1.457 1.316
##  [517] 1.367 1.487 1.325 1.330 1.321 1.418 1.417 1.489 1.388 1.448 1.344 1.350
##  [529] 1.410 1.317 1.464 1.458 1.361 1.376 1.438 1.317 1.414 1.448 1.316 1.329
##  [541] 1.409 1.321 1.455 1.389 1.324 1.444 1.405 1.451 1.421 1.365 1.330 1.367
##  [553] 1.486 1.469 1.376 1.328 1.342 1.404 1.312 1.313 1.362 1.354 1.347 1.332
##  [565] 1.420 1.322 1.484 1.428 1.365 1.372 1.458 1.416 1.473 1.473 1.342 1.418
##  [577] 1.363 1.402 1.428 1.423 1.451 1.369 1.368 1.401 1.350 1.338 1.445 1.317
##  [589] 1.359 1.338 1.429 1.493 1.408 1.321 1.315 1.425 1.447 1.326 1.479 1.422
##  [601] 1.477 1.363 1.442 1.400 1.365 1.379 1.352 1.331 1.467 1.328 1.373 1.491
##  [613] 1.430 1.493 1.445 1.425 1.385 1.433 1.486 1.397 1.345 1.403 1.398 1.439
##  [625] 1.378 1.362 1.370 1.319 1.404 1.422 1.390 1.398 1.416 1.466 1.446 1.402
##  [637] 1.451 1.386 1.316 1.366 1.425 1.343 1.414 1.430 1.466 1.339 1.404 1.339
##  [649] 1.486 1.443 1.317 1.362 1.389 1.386 1.403 1.347 1.428 1.315 1.396 1.415
##  [661] 1.368 1.480 1.396 1.455 1.480 1.444 1.453 1.462 1.340 1.341 1.335 1.315
##  [673] 1.319 1.405 1.327 1.379 1.363 1.372 1.487 1.361 1.348 1.315 1.367 1.410
##  [685] 1.435 1.390 1.341 1.414 1.438 1.380 1.429 1.399 1.324 1.448 1.320 1.375
##  [697] 1.410 1.474 1.410 1.431 1.412 1.475 1.454 1.370 1.415 1.371 1.359 1.453
##  [709] 1.411 1.391 1.374 1.355 1.439 1.411 1.449 1.316 1.458 1.396 1.360 1.331
##  [721] 1.421 1.387 1.365 1.312 1.458 1.318 1.434 1.390 1.352 1.366 1.464 1.319
##  [733] 1.479 1.464 1.417 1.438 1.361 1.371 1.435 1.360 1.441 1.330 1.411 1.332
##  [745] 1.404 1.329 1.336 1.313 1.375 1.375 1.312 1.491 1.373 1.376 1.380 1.342
##  [757] 1.474 1.411 1.314 1.335 1.327 1.335 1.349 1.369 1.475 1.344 1.481 1.430
##  [769] 1.343 1.406 1.337 1.316 1.380 1.432 1.431 1.477 1.456 1.412 1.398 1.484
##  [781] 1.481 1.341 1.329 1.449 1.380 1.401 1.429 1.402 1.371 1.426 1.439 1.326
##  [793] 1.327 1.441 1.460 1.462 1.437 1.414 1.493 1.335 1.374 1.344 1.344 1.484
##  [805] 1.490 1.373 1.313 1.393 1.392 1.334 1.371 1.450 1.465 1.351 1.358 1.345
##  [817] 1.361 1.397 1.383 1.438 1.471 1.485 1.376 1.481 1.439 1.473 1.462 1.390
##  [829] 1.453 1.471 1.445 1.458 1.367 1.392 1.363 1.329 1.431 1.491 1.446 1.321
##  [841] 1.393 1.466 1.376 1.432 1.341 1.416 1.324 1.483 1.469 1.483 1.476 1.339
##  [853] 1.487 1.425 1.395 1.434 1.411 1.351 1.414 1.466 1.345 1.438 1.370 1.313
##  [865] 1.396 1.489 1.492 1.325 1.327 1.447 1.458 1.485 1.419 1.479 1.484 1.410
##  [877] 1.468 1.374 1.426 1.424 1.389 1.429 1.405 1.374 1.402 1.457 1.424 1.476
##  [889] 1.325 1.392 1.343 1.463 1.321 1.449 1.467 1.425 1.475 1.373 1.438 1.411
##  [901] 1.367 1.437 1.341 1.492 1.374 1.350 1.411 1.446 1.465 1.407 1.411 1.322
##  [913] 1.401 1.387 1.315 1.475 1.404 1.368 1.380 1.396 1.372 1.375 1.419 1.475
##  [925] 1.397 1.368 1.425 1.386 1.379 1.315 1.447 1.465 1.458 1.343 1.342 1.412
##  [937] 1.427 1.447 1.366 1.411 1.383 1.411 1.325 1.392 1.404 1.394 1.395 1.459
##  [949] 1.343 1.399 1.446 1.445 1.489 1.434 1.371 1.382 1.483 1.341 1.448 1.341
##  [961] 1.392 1.470 1.474 1.448 1.467 1.491 1.386 1.386 1.389 1.426 1.357 1.335
##  [973] 1.467 1.486 1.411 1.492 1.370 1.431 1.424 1.484 1.410 1.380 1.358 1.444
##  [985] 1.343 1.474 1.473 1.470 1.417 1.373 1.373 1.417 1.420 1.430 1.383 1.341
##  [997] 1.410 1.462 1.467 1.424 1.446 1.468 1.482 1.474 1.412 1.462 1.451 1.410
## [1009] 1.322 1.362 1.423 1.485 1.374 1.479 1.351 1.392 1.351 1.490 1.474 1.341
## [1021] 1.374 1.475 1.446 1.452 1.411 1.394 1.482 1.442 1.412 1.439 1.457 1.462
## [1033] 1.351 1.487 1.464 1.341 1.479 1.362 1.315 1.411 1.383 1.398 1.400 1.454
## [1045] 1.353 1.474 1.374 1.411 1.464 1.411 1.374 1.419 1.429 1.476 1.390 1.316
## [1057] 1.334 1.380 1.411 1.323 1.343 1.464 1.396 1.467 1.411 1.452 1.343 1.321
## [1069] 1.320 1.479 1.316 1.317 1.312 1.312 1.366 1.397 1.370 1.383 1.400 1.401
## [1081] 1.423 1.380 1.366 1.397 1.407 1.415 1.420 1.413 1.315 1.400 1.315 1.351
## [1093] 1.370 1.347 1.354 1.434 1.333 1.435 1.421 1.490 1.320 1.480 1.477 1.366
## [1105] 1.385 1.382 1.418 1.368 1.486 1.462 1.470 1.376 1.323 1.399 1.325 1.318
## [1117] 1.365 1.339 1.366 1.344 1.377 1.327 1.374 1.316 1.357 1.370 1.334 1.318
## [1129] 1.350 1.397 1.317 1.419 1.320 1.372 1.312 1.385 1.361 1.353 1.388 1.385
## [1141] 1.325 1.480 1.363 1.487 1.326 1.312 1.419 1.358 1.373 1.320 1.482 1.473
## [1153] 1.451 1.371 1.459 1.342 1.408 1.345 1.327 1.484 1.464 1.375 1.339 1.439
## [1165] 1.489 1.323 1.362 1.329 1.395 1.354 1.404 1.434 1.330 1.351 1.318 1.407
## [1177] 1.488 1.486 1.477 1.354 1.407 1.372 1.372 1.383 1.400 1.418 1.350 1.434
## [1189] 1.416 1.326 1.312 1.378 1.464 1.437 1.410 1.404 1.422 1.374 1.480 1.423
## [1201] 1.367 1.389 1.334 1.345 1.461 1.445 1.332 1.369 1.361 1.429 1.319 1.348
## [1213] 1.409 1.384 1.449 1.432 1.389 1.371 1.396 1.313 1.490 1.462 1.478 1.361
## [1225] 1.408 1.349 1.378 1.468 1.440 1.446 1.454 1.462 1.437 1.416 1.426 1.471
## [1237] 1.424 1.419 1.353 1.362 1.401 1.412 1.395 1.476 1.403 1.466 1.452 1.375
## [1249] 1.486 1.385 1.459 1.454 1.436 1.367 1.326 1.463 1.330 1.410 1.484 1.393
## [1261] 1.343 1.487 1.433 1.397 1.396 1.395 1.428 1.491 1.470 1.413 1.387 1.463
## [1273] 1.465 1.453 1.414 1.329 1.370 1.438 1.475 1.469 1.477 1.341 1.474 1.462
## [1285] 1.387 1.456 1.337 1.442 1.470 1.355 1.424 1.424 1.347 1.438 1.374 1.410
## [1297] 1.418 1.322 1.343 1.327 1.336 1.438 1.419 1.397 1.449 1.467 1.419 1.490
## [1309] 1.321 1.491 1.386 1.488 1.453 1.411 1.453 1.463 1.472 1.482 1.318 1.453
## [1321] 1.431 1.480 1.346 1.475 1.482 1.322 1.375 1.333 1.453 1.357 1.431 1.411
## [1333] 1.375 1.477 1.487 1.405 1.483 1.416 1.360 1.374 1.341 1.410 1.414 1.381
## [1345] 1.358 1.459 1.424 1.445 1.431 1.410 1.317 1.407 1.481 1.457 1.438 1.424
## [1357] 1.475 1.422 1.438 1.411 1.444 1.374 1.411 1.487 1.374 1.492 1.334 1.396
## [1369] 1.422 1.470 1.388 1.374 1.384 1.351 1.452 1.442 1.377 1.317 1.466 1.401
## [1381] 1.395 1.393 1.468 1.384 1.478 1.459 1.422 1.316 1.432 1.350 1.327 1.488
## [1393] 1.423 1.447 1.385 1.432 1.406 1.489 1.373 1.348 1.434 1.432 1.380 1.455
## [1405] 1.344 1.324 1.387 1.457 1.390 1.416 1.428 1.390 1.453 1.382 1.324 1.396
## [1417] 1.352 1.388 1.424 1.462 1.374 1.388 1.380 1.327 1.476 1.336 1.398 1.331
## [1429] 1.492 1.366 1.332 1.314 1.328 1.313 1.397 1.477 1.443 1.466 1.405 1.435
## [1441] 1.410 1.397 1.493 1.430 1.450 1.329 1.327 1.366 1.319 1.406 1.348 1.430
## [1453] 1.454 1.425 1.481 1.361 1.430 1.365 1.492 1.361 1.329 1.474 1.327 1.321
## [1465] 1.411 1.360 1.367 1.417 1.443 1.490 1.388 1.370 1.330 1.463 1.435
rem_out3_kud95 <- subset(week_kuds, KUD95 < 1.311)
boxplot(rem_out3_kud95$KUD95, col = "deepskyblue", ylab="KUD95", main="Boxplot of KUD95 after the third outliers removal")

boxplot.stats(rem_out2_kud50$KUD50) #superior limit where outliers start is 0.233
## $stats
## [1] 0.162 0.164 0.166 0.192 0.233
## 
## $n
## [1] 21213
## 
## $conf
## [1] 0.1656963 0.1663037
## 
## $out
##    [1] 0.245 0.251 0.256 0.241 0.255 0.274 0.270 0.262 0.273 0.240 0.277 0.243
##   [13] 0.248 0.257 0.246 0.243 0.280 0.237 0.257 0.235 0.252 0.246 0.257 0.277
##   [25] 0.250 0.249 0.266 0.279 0.273 0.276 0.266 0.251 0.272 0.270 0.243 0.245
##   [37] 0.264 0.273 0.271 0.234 0.235 0.252 0.269 0.254 0.260 0.259 0.250 0.269
##   [49] 0.252 0.245 0.237 0.277 0.256 0.279 0.278 0.258 0.246 0.240 0.245 0.245
##   [61] 0.270 0.247 0.273 0.272 0.263 0.255 0.258 0.251 0.261 0.243 0.245 0.234
##   [73] 0.257 0.259 0.260 0.275 0.234 0.265 0.248 0.234 0.269 0.235 0.249 0.279
##   [85] 0.249 0.236 0.252 0.271 0.273 0.241 0.279 0.234 0.249 0.248 0.279 0.266
##   [97] 0.251 0.276 0.261 0.239 0.271 0.271 0.247 0.234 0.242 0.265 0.249 0.253
##  [109] 0.272 0.248 0.276 0.276 0.238 0.234 0.269 0.269 0.240 0.264 0.267 0.277
##  [121] 0.242 0.241 0.240 0.252 0.276 0.271 0.275 0.250 0.263 0.261 0.252 0.270
##  [133] 0.271 0.260 0.277 0.261 0.269 0.279 0.275 0.274 0.265 0.271 0.276 0.275
##  [145] 0.273 0.276 0.275 0.236 0.236 0.262 0.278 0.272 0.242 0.265 0.252 0.259
##  [157] 0.267 0.254 0.278 0.263 0.270 0.252 0.268 0.273 0.276 0.279 0.275 0.254
##  [169] 0.270 0.255 0.239 0.260 0.251 0.252 0.251 0.251 0.280 0.245 0.244 0.255
##  [181] 0.247 0.271 0.269 0.276 0.237 0.258 0.264 0.272 0.249 0.248 0.241 0.256
##  [193] 0.244 0.247 0.274 0.265 0.264 0.242 0.259 0.252 0.268 0.279 0.242 0.269
##  [205] 0.259 0.248 0.235 0.276 0.253 0.276 0.274 0.245 0.260 0.260 0.270 0.265
##  [217] 0.260 0.265 0.251 0.271 0.253 0.260 0.245 0.235 0.258 0.237 0.278 0.265
##  [229] 0.248 0.264 0.252 0.256 0.236 0.254 0.272 0.272 0.277 0.278 0.248 0.242
##  [241] 0.274 0.271 0.247 0.270 0.252 0.242 0.258 0.271 0.258 0.258 0.236 0.275
##  [253] 0.259 0.234 0.276 0.240 0.248 0.260 0.272 0.278 0.245 0.247 0.244 0.278
##  [265] 0.280 0.246 0.273 0.257 0.268 0.262 0.278 0.276 0.239 0.245 0.253 0.268
##  [277] 0.234 0.251 0.255 0.280 0.273 0.252 0.251 0.260 0.254 0.241 0.237 0.254
##  [289] 0.249 0.262 0.246 0.236 0.236 0.277 0.268 0.249 0.238 0.256 0.246 0.250
##  [301] 0.240 0.258 0.257 0.251 0.255 0.243 0.247 0.236 0.238 0.277 0.248 0.250
##  [313] 0.272 0.238 0.254 0.235 0.237 0.234 0.277 0.274 0.243 0.272 0.277 0.252
##  [325] 0.249 0.249 0.260 0.274 0.279 0.273 0.278 0.265 0.254 0.273 0.269 0.251
##  [337] 0.267 0.237 0.235 0.253 0.273 0.273 0.252 0.254 0.254 0.259 0.263 0.268
##  [349] 0.250 0.236 0.243 0.252 0.239 0.269 0.253 0.261 0.252 0.269 0.269 0.235
##  [361] 0.272 0.265 0.251 0.244 0.272 0.251 0.259 0.248 0.268 0.257 0.254 0.267
##  [373] 0.276 0.247 0.275 0.239 0.236 0.266 0.273 0.245 0.239 0.235 0.268 0.274
##  [385] 0.245 0.257 0.266 0.236 0.259 0.248 0.245 0.274 0.256 0.244 0.270 0.239
##  [397] 0.276 0.250 0.273 0.261 0.267 0.255 0.268 0.259 0.248 0.238 0.278 0.234
##  [409] 0.259 0.247 0.244 0.278 0.274 0.278 0.279 0.264 0.251 0.238 0.239 0.238
##  [421] 0.242 0.243 0.236 0.242 0.265 0.256 0.237 0.258 0.248 0.247 0.245 0.258
##  [433] 0.259 0.265 0.274 0.248 0.243 0.253 0.240 0.234 0.260 0.239 0.269 0.242
##  [445] 0.268 0.262 0.237 0.244 0.234 0.245 0.256 0.238 0.250 0.257 0.274 0.257
##  [457] 0.261 0.248 0.268 0.262 0.262 0.274 0.250 0.279 0.254 0.235 0.262 0.245
##  [469] 0.246 0.277 0.250 0.238 0.235 0.245 0.254 0.250 0.244 0.235 0.237 0.238
##  [481] 0.241 0.242 0.243 0.237 0.235 0.257 0.270 0.245 0.277 0.249 0.263 0.271
##  [493] 0.274 0.244 0.259 0.263 0.249 0.249 0.271 0.246 0.240 0.245 0.246 0.268
##  [505] 0.265 0.243 0.263 0.268 0.239 0.271 0.244 0.238 0.256 0.259 0.260 0.273
##  [517] 0.253 0.253 0.268 0.272 0.247 0.240 0.247 0.270 0.241 0.269 0.245 0.247
##  [529] 0.240 0.248 0.261 0.240 0.241 0.249 0.244 0.240 0.240 0.255 0.238 0.236
##  [541] 0.249 0.263 0.244 0.237 0.237 0.245 0.257 0.240 0.273 0.253 0.244 0.279
##  [553] 0.240 0.239 0.255 0.241 0.248 0.257 0.234 0.239 0.280 0.245 0.251 0.248
##  [565] 0.246 0.278 0.246 0.251 0.241 0.252 0.247 0.256 0.245 0.260 0.260 0.238
##  [577] 0.251 0.252 0.269 0.275 0.272 0.261 0.236 0.246 0.256 0.247 0.237 0.242
##  [589] 0.240 0.260 0.246 0.276 0.249 0.263 0.254 0.271 0.237 0.261 0.260 0.258
##  [601] 0.249 0.249 0.260 0.249 0.235 0.249 0.234 0.256 0.260 0.253 0.276 0.270
##  [613] 0.244 0.239 0.263 0.246 0.270 0.263 0.268 0.265 0.244 0.276 0.252 0.248
##  [625] 0.249 0.259 0.263 0.247 0.269 0.236 0.266 0.254 0.272 0.252 0.254 0.259
##  [637] 0.256 0.257 0.272 0.269 0.279 0.242 0.272 0.234 0.270 0.255 0.257 0.269
##  [649] 0.241 0.262 0.250 0.261 0.262 0.252 0.258 0.241 0.262 0.240 0.249 0.244
##  [661] 0.245 0.248 0.277 0.255 0.247 0.242 0.261 0.249 0.235 0.237 0.240 0.253
##  [673] 0.238 0.235 0.243 0.241 0.238 0.245 0.274 0.251 0.234 0.242 0.239 0.254
##  [685] 0.238 0.243 0.238 0.254 0.252 0.235 0.240 0.246 0.266 0.268 0.255 0.252
##  [697] 0.253 0.264 0.242 0.238 0.247 0.258 0.245 0.280 0.243 0.236 0.249 0.237
##  [709] 0.236 0.245 0.259 0.234 0.255 0.237 0.234 0.265 0.238 0.240 0.276 0.269
##  [721] 0.248 0.252 0.259 0.260 0.256 0.270 0.277 0.246 0.253 0.235 0.235 0.270
##  [733] 0.273 0.242 0.272 0.239 0.273 0.280 0.273 0.270 0.235 0.240 0.266 0.255
##  [745] 0.276 0.248 0.249 0.268 0.248 0.259 0.240 0.238 0.265 0.269 0.255 0.244
##  [757] 0.266 0.253 0.279 0.266 0.251 0.275 0.274 0.262 0.251 0.259 0.248 0.246
##  [769] 0.237 0.240 0.248 0.253 0.238 0.234 0.249 0.246 0.249 0.252 0.236 0.234
##  [781] 0.258 0.252 0.234 0.253 0.246 0.257 0.277 0.242 0.240 0.241 0.244 0.235
##  [793] 0.252 0.254 0.251 0.249 0.254 0.258 0.251 0.244 0.247 0.234 0.248 0.254
##  [805] 0.271 0.246 0.256 0.277 0.246 0.258 0.257 0.265 0.256 0.242 0.250 0.253
##  [817] 0.238 0.247 0.255 0.245 0.245 0.238 0.237 0.238 0.247 0.273 0.250 0.241
##  [829] 0.250 0.264 0.238 0.240 0.258 0.250 0.241 0.247 0.243 0.246 0.280 0.235
##  [841] 0.266 0.279 0.264 0.249 0.238 0.273 0.262 0.265 0.270 0.256 0.267 0.264
##  [853] 0.277 0.262 0.234 0.253 0.255 0.253 0.259 0.257 0.255 0.257 0.238 0.276
##  [865] 0.274 0.278 0.262 0.272 0.277 0.243 0.264 0.270 0.236 0.241 0.239 0.255
##  [877] 0.275 0.244 0.277 0.253 0.237 0.240 0.237 0.247 0.235 0.263 0.236 0.240
##  [889] 0.259 0.248 0.274 0.259 0.234 0.280 0.238 0.257 0.260 0.234 0.249 0.265
##  [901] 0.237 0.277 0.254 0.277 0.267 0.251 0.248 0.239 0.266 0.276 0.238 0.258
##  [913] 0.243 0.266 0.276 0.263 0.262 0.265 0.248 0.267 0.244 0.244 0.257 0.271
##  [925] 0.254 0.264 0.258 0.268 0.236 0.267 0.254 0.236 0.257 0.269 0.257 0.249
##  [937] 0.258 0.243 0.237 0.243 0.245 0.249 0.250 0.234 0.264 0.265 0.255 0.234
##  [949] 0.251 0.267 0.237 0.250 0.247 0.268 0.276 0.272 0.261 0.255 0.253 0.244
##  [961] 0.244 0.250 0.256 0.244 0.279 0.279 0.246 0.279 0.276 0.249 0.275 0.247
##  [973] 0.246 0.241 0.274 0.274 0.245 0.235 0.253 0.262 0.262 0.262 0.262 0.263
##  [985] 0.268 0.238 0.273 0.237 0.263 0.247 0.262 0.242 0.273 0.271 0.243 0.267
##  [997] 0.235 0.268 0.260 0.260 0.246 0.238 0.238 0.248 0.239 0.253 0.251 0.255
## [1009] 0.254 0.242 0.259 0.239 0.251 0.253 0.275 0.252 0.237 0.260 0.242 0.268
## [1021] 0.248 0.256 0.236 0.250 0.262 0.241 0.234 0.263 0.237 0.238 0.237 0.277
## [1033] 0.244 0.259 0.275 0.244 0.247 0.248 0.253 0.255 0.244 0.263 0.243 0.253
## [1045] 0.259 0.253 0.239 0.238 0.250 0.258 0.252 0.278 0.235 0.237 0.246 0.236
## [1057] 0.269 0.234 0.234 0.247 0.244 0.238 0.239 0.241 0.256 0.242 0.237 0.240
## [1069] 0.235 0.252 0.237 0.258 0.251 0.258 0.275 0.247 0.270 0.251 0.243 0.267
## [1081] 0.252 0.253 0.261 0.246 0.257 0.249 0.254 0.241 0.271 0.237 0.260 0.243
## [1093] 0.244 0.253 0.244 0.248 0.245 0.252 0.261 0.272 0.240 0.239 0.243 0.252
## [1105] 0.236 0.237 0.238 0.242 0.253 0.250 0.238 0.251 0.267 0.248 0.274 0.256
## [1117] 0.262 0.257 0.268 0.238 0.247 0.235 0.244 0.246 0.238 0.274 0.241 0.278
## [1129] 0.271 0.272 0.238 0.257 0.241 0.279 0.269 0.250 0.255 0.236 0.240 0.235
## [1141] 0.270 0.243 0.264 0.277 0.276 0.257 0.258 0.240 0.240 0.241 0.244 0.238
## [1153] 0.242 0.252 0.255 0.241 0.267 0.243 0.274 0.253 0.236 0.247 0.265 0.277
## [1165] 0.247 0.239 0.243 0.241 0.263 0.247 0.245 0.241 0.266 0.238 0.235 0.273
## [1177] 0.234 0.266 0.243 0.243 0.257 0.241 0.275 0.257 0.261 0.280 0.277 0.274
## [1189] 0.260 0.256 0.272 0.265 0.243 0.246 0.266 0.260 0.274 0.238 0.238 0.262
## [1201] 0.240 0.263 0.234 0.238 0.253 0.243 0.245 0.239 0.235 0.236 0.267 0.235
## [1213] 0.267 0.250 0.254 0.241 0.236 0.261 0.238 0.252 0.248 0.258 0.255 0.275
## [1225] 0.252 0.249 0.241 0.268 0.234 0.252 0.256 0.234 0.266 0.245 0.256 0.254
## [1237] 0.253 0.275 0.274 0.239 0.248 0.253 0.275 0.245 0.275 0.249 0.275 0.259
## [1249] 0.243 0.276 0.240 0.255 0.246 0.255 0.239 0.259 0.234 0.266 0.268 0.245
## [1261] 0.240 0.240 0.258 0.259 0.259 0.235 0.270 0.252 0.268 0.257 0.250 0.238
## [1273] 0.254 0.266 0.244 0.270 0.253 0.265 0.245 0.263 0.237 0.244 0.236 0.272
## [1285] 0.275 0.237 0.266 0.273 0.260 0.243 0.239 0.247 0.234 0.250 0.235 0.263
## [1297] 0.264 0.269 0.266 0.275 0.242 0.235 0.265 0.239 0.250 0.255 0.254 0.261
## [1309] 0.247 0.252 0.256 0.266 0.261 0.261 0.255 0.238 0.257 0.269 0.275 0.249
## [1321] 0.267 0.244 0.243 0.271 0.271 0.260 0.268 0.266 0.242 0.263 0.239 0.256
## [1333] 0.247 0.277 0.235 0.235 0.239 0.259 0.236 0.239 0.243 0.272 0.251 0.260
## [1345] 0.243 0.244 0.235 0.241 0.270 0.263 0.262 0.251 0.266 0.245 0.236 0.244
## [1357] 0.247 0.275 0.270 0.244 0.243 0.251 0.246 0.261 0.242 0.253 0.269 0.256
## [1369] 0.242 0.269 0.243 0.263 0.240 0.266 0.236 0.258 0.274 0.267 0.238 0.256
## [1381] 0.239 0.252 0.245 0.260 0.237 0.254 0.246 0.237 0.249 0.279 0.275 0.279
## [1393] 0.268 0.279 0.253 0.269 0.250 0.245 0.243 0.264 0.237 0.238 0.267 0.275
## [1405] 0.265 0.242 0.250 0.262 0.254 0.242 0.245 0.243 0.272 0.255 0.269 0.244
## [1417] 0.246 0.244 0.246 0.249 0.258 0.275 0.235 0.252 0.268 0.258 0.265 0.236
## [1429] 0.269 0.240 0.243 0.236 0.272 0.254 0.252 0.268 0.236 0.274 0.256 0.263
## [1441] 0.239 0.246 0.263 0.276 0.277 0.240 0.241 0.261 0.259 0.234 0.250 0.269
## [1453] 0.238 0.274 0.238 0.247 0.237 0.264 0.253 0.240 0.253 0.263 0.242 0.254
## [1465] 0.238 0.251 0.238 0.249 0.278 0.234 0.260 0.242 0.250 0.260 0.257 0.271
## [1477] 0.241 0.239 0.280 0.262 0.236 0.238 0.263 0.260 0.243 0.241 0.247 0.238
## [1489] 0.277 0.278 0.277 0.246 0.271 0.247 0.278 0.248 0.278 0.255 0.268 0.250
## [1501] 0.263 0.264 0.234 0.243 0.246 0.258 0.237 0.242 0.258 0.264 0.275 0.278
## [1513] 0.249 0.249 0.240 0.252 0.258 0.267 0.254 0.255 0.234 0.260 0.249 0.242
## [1525] 0.275 0.250 0.246 0.267 0.238 0.239 0.248 0.269 0.253 0.271 0.258 0.239
## [1537] 0.265 0.243 0.238 0.243 0.236 0.249 0.251 0.243 0.269 0.239 0.247 0.280
## [1549] 0.270 0.263 0.251 0.274 0.277 0.251 0.253 0.280 0.277 0.272 0.247 0.276
## [1561] 0.248 0.274 0.273 0.237 0.264 0.251 0.260 0.268 0.238 0.259 0.270 0.264
## [1573] 0.251 0.235 0.268 0.279 0.251 0.237 0.242 0.261 0.269 0.238 0.261 0.269
## [1585] 0.259 0.271 0.255 0.250 0.276 0.245 0.263 0.248 0.254 0.270 0.279 0.265
## [1597] 0.261 0.250 0.277 0.262 0.279 0.238 0.245 0.240 0.275 0.270 0.257 0.266
## [1609] 0.245 0.236 0.271 0.235 0.242 0.266 0.253 0.242 0.244 0.272 0.256 0.264
## [1621] 0.234 0.249 0.253 0.257 0.235 0.261 0.252 0.237 0.236 0.274 0.270 0.273
## [1633] 0.236 0.236 0.241 0.257 0.274 0.247 0.244 0.264 0.253 0.234 0.246 0.261
## [1645] 0.263 0.273 0.234 0.237 0.243 0.241 0.253 0.252 0.235 0.236 0.249 0.240
## [1657] 0.266 0.266 0.245 0.264 0.277 0.272 0.268 0.254 0.239 0.262 0.275 0.245
## [1669] 0.247 0.241 0.266 0.250 0.257 0.265 0.251 0.240 0.234 0.274 0.275 0.270
## [1681] 0.253 0.255 0.260 0.242 0.256 0.266 0.238 0.277 0.248 0.280 0.254 0.274
## [1693] 0.280 0.267 0.237 0.240 0.235 0.251 0.236 0.236 0.270 0.251 0.279 0.277
## [1705] 0.268 0.263 0.280 0.257 0.263 0.278 0.251 0.276 0.267 0.250 0.236 0.242
## [1717] 0.237 0.255 0.265 0.271 0.248 0.271 0.266 0.240 0.277 0.256 0.279 0.244
## [1729] 0.249 0.242 0.262 0.267 0.259 0.251 0.253 0.235 0.248 0.252 0.238 0.237
## [1741] 0.269 0.250 0.272 0.279 0.251 0.263 0.253 0.253 0.272 0.253 0.256 0.240
## [1753] 0.251 0.253 0.255 0.260 0.241 0.273 0.270 0.279 0.240 0.247 0.243 0.268
## [1765] 0.246 0.239 0.241 0.264 0.270 0.250 0.250 0.247 0.250 0.256 0.242 0.238
## [1777] 0.238 0.268 0.245 0.244 0.236 0.247 0.236 0.234 0.255 0.244 0.261 0.279
## [1789] 0.270 0.248 0.261 0.254 0.256 0.246 0.278 0.250 0.238 0.250 0.239 0.250
## [1801] 0.263 0.238 0.250 0.270 0.234 0.260 0.243 0.246 0.253 0.257 0.253 0.237
## [1813] 0.244 0.250 0.238 0.238 0.268 0.255 0.258 0.250 0.249 0.249 0.254 0.263
## [1825] 0.270 0.278 0.251 0.267 0.252 0.266 0.238 0.248 0.245 0.239 0.252 0.268
## [1837] 0.275 0.261 0.242 0.240 0.244 0.247 0.240 0.239 0.235 0.260 0.265 0.244
## [1849] 0.253 0.267 0.278 0.237 0.260 0.254 0.239 0.246 0.245 0.279 0.268 0.278
## [1861] 0.278 0.253 0.278 0.267 0.251 0.237 0.235 0.252 0.236 0.251 0.255 0.260
## [1873] 0.263 0.238 0.234 0.253 0.278 0.237 0.254 0.238 0.243 0.279 0.237 0.263
## [1885] 0.259 0.238 0.270 0.252 0.239 0.242 0.276 0.253 0.242 0.245 0.242 0.234
## [1897] 0.261 0.236 0.242 0.279 0.271 0.240 0.236 0.239 0.258 0.235 0.236 0.264
## [1909] 0.235 0.260 0.278 0.268 0.257 0.236 0.258 0.241 0.263 0.262 0.276 0.273
## [1921] 0.269 0.272 0.273 0.278 0.270 0.275 0.251 0.274 0.276 0.275 0.277 0.275
## [1933] 0.272 0.275 0.261 0.264 0.256 0.261 0.255 0.273 0.272 0.270 0.270 0.262
## [1945] 0.273 0.270 0.250 0.272 0.269 0.268 0.271 0.271 0.251 0.273 0.269 0.269
## [1957] 0.247 0.266 0.271 0.264 0.242 0.274 0.263 0.264 0.241 0.265 0.238 0.280
## [1969] 0.270 0.268 0.257 0.266 0.258 0.271 0.270 0.271 0.239 0.238 0.273 0.258
## [1981] 0.265 0.236 0.254 0.242 0.243 0.247 0.252 0.277 0.266 0.257 0.280 0.267
## [1993] 0.259 0.240 0.261 0.234 0.249 0.252 0.257 0.258 0.254 0.254 0.267 0.271
## [2005] 0.276 0.260 0.272 0.261 0.266 0.237 0.266 0.265 0.246 0.272 0.280 0.236
## [2017] 0.258 0.271 0.249 0.245 0.269 0.277 0.247 0.273 0.275 0.279 0.250 0.251
## [2029] 0.261 0.264 0.245 0.245 0.237 0.275 0.242 0.277 0.238 0.266 0.234 0.254
## [2041] 0.260 0.277 0.253 0.244 0.253 0.247 0.249 0.261 0.253 0.280 0.241 0.252
## [2053] 0.247 0.264 0.248 0.267 0.260 0.256 0.248 0.261 0.278 0.249 0.261 0.250
## [2065] 0.252 0.252 0.243 0.265 0.243 0.234 0.256 0.236 0.274 0.237 0.270 0.248
## [2077] 0.247 0.272 0.269 0.263 0.244 0.242 0.243 0.254 0.278 0.254 0.247 0.271
## [2089] 0.254 0.238 0.236 0.243 0.249 0.241 0.261 0.253 0.246 0.264 0.260 0.258
## [2101] 0.278 0.274 0.264 0.265 0.271 0.244 0.240 0.267 0.252 0.255 0.234 0.241
## [2113] 0.242 0.241 0.252 0.279 0.242 0.261 0.252 0.251 0.248 0.252 0.242 0.245
## [2125] 0.246 0.245 0.253 0.270 0.242 0.239 0.256 0.244 0.249 0.261 0.246 0.243
## [2137] 0.272 0.274 0.241 0.276 0.279 0.274 0.251 0.272 0.273 0.268 0.262 0.235
## [2149] 0.267 0.237 0.260 0.247 0.246 0.246 0.277 0.261 0.239 0.265 0.264 0.257
## [2161] 0.249 0.239 0.248 0.278 0.269 0.279 0.275 0.263 0.255 0.250 0.270 0.244
## [2173] 0.244 0.247 0.272 0.238 0.248 0.241 0.251 0.269 0.241 0.254 0.238 0.254
## [2185] 0.261 0.258 0.267 0.251 0.246 0.279 0.276 0.280 0.251 0.246 0.238 0.278
## [2197] 0.236 0.239 0.258 0.245 0.250 0.248 0.239 0.236 0.280 0.252 0.250 0.244
## [2209] 0.276 0.242 0.248 0.237 0.252 0.237 0.254 0.270 0.261 0.240 0.275 0.239
## [2221] 0.254 0.265 0.265 0.251 0.269 0.259 0.274 0.245 0.242 0.237 0.258 0.276
## [2233] 0.258 0.239 0.272 0.246 0.261 0.280 0.239 0.263 0.247 0.259 0.254 0.260
## [2245] 0.280 0.269 0.270 0.238 0.268 0.269 0.239 0.252 0.273 0.272 0.260 0.239
## [2257] 0.251 0.269 0.272 0.252 0.259 0.273 0.260 0.256 0.270 0.260 0.266 0.266
## [2269] 0.243 0.261 0.265 0.259 0.266 0.274 0.241 0.275 0.277 0.235 0.257 0.234
## [2281] 0.258 0.246 0.239 0.255 0.276 0.236 0.241 0.274 0.254 0.240 0.276 0.279
## [2293] 0.237 0.251 0.237 0.256 0.250 0.256 0.256 0.258 0.244 0.249 0.237 0.259
## [2305] 0.248 0.241 0.278 0.273 0.267 0.264 0.261 0.246 0.257 0.240 0.256 0.254
## [2317] 0.265 0.238 0.256 0.246 0.257 0.256 0.243 0.250 0.278 0.244 0.245 0.248
## [2329] 0.265 0.267 0.262 0.246 0.279
rem_out3_kud50 <- subset(week_kuds, KUD50 < 0.233)
boxplot(rem_out3_kud50$KUD50, col = "green2", ylab="KUD50", main="Boxplot of KUD50 after the third outliers removal")

As the distributions of both KUD95 and KUD50 kept the outliers after consecutive removal, we decided to keep all observations in the dataset. This happened probably because the data has a high variability that is not explained by a normal distribution, so the outliers are not erros but in fact variability that the normal distribution can not explain but that hold relevant biological information that we should not ignore.

Linearity Assumption

KUD95

#Correlation between response and numeric explanatory variables
plot(week_kuds$LengthStd, week_kuds$KUD95, xlab = "Length Std", ylab="KUD95", col="deepskyblue", main = "KUD95 as a function of Length Std")

plot(week_kuds$BodyMassStd, week_kuds$KUD95, xlab = "Body Mass Std", ylab="KUD95", col="deepskyblue", main = "KUD95 as a function of Body Mass Std")

plot(week_kuds$Longevity, week_kuds$KUD95, xlab = "Longevity", ylab="KUD95", col="deepskyblue", main = "KUD95 as a function of Longevity")

plot(week_kuds$Vulnerability, week_kuds$KUD95, xlab = "Vulnerability", ylab="KUD95", col="deepskyblue", main = "KUD95 as a function of Vulnerability")

plot(week_kuds$Troph, week_kuds$KUD95, xlab = "Troph", ylab="KUD95", col="deepskyblue", main = "KUD95 as a function of Troph")

plot(week_kuds$ReceiverDensity, week_kuds$KUD95, xlab = "Receiver Density", ylab="KUD95", col="deepskyblue", main = "KUD95 as a function of Receiver Density")

plot(week_kuds$MonitArea_km2, week_kuds$KUD95, xlab = "MonitArea (km2)", ylab="KUD95", col="deepskyblue", main = "KUD95 as a function of Monitored Area")

plot(week_kuds$MCP_km2, week_kuds$KUD95, xlab = "MCP (km2)", ylab="KUD95", col="deepskyblue", main = "KUD95 as a function of MCP")

plot(week_kuds$NReceivers, week_kuds$KUD95, xlab = "N Receivers", ylab="KUD95", col="deepskyblue", main = "KUD95 as a function of N Receivers")

plot(week_kuds$MaxDistReceivers, week_kuds$KUD95, xlab = "Max. Dist. Receivers", ylab="KUD95", col="deepskyblue", main = "KUD95 as a function of Max. Dist. Receivers")

cor(week_kuds$LengthStd, week_kuds$KUD95)
## [1] -0.02151777
cor(week_kuds$BodyMassStd, week_kuds$KUD95)
## [1] -0.0553196
cor(week_kuds$Longevity, week_kuds$KUD95)
## [1] -0.08065923
cor(week_kuds$Vulnerability, week_kuds$KUD95)
## [1] -0.04922472
cor(week_kuds$Troph, week_kuds$KUD95)
## [1] 0.06083729
cor(week_kuds$ReceiverDensity, week_kuds$KUD95)
## [1] -0.12147
cor(week_kuds$MonitArea_km2, week_kuds$KUD95)
## [1] 0.3207865
cor(week_kuds$MCP_km2, week_kuds$KUD95)
## [1] 0.0804618
cor(week_kuds$NReceivers, week_kuds$KUD95)
## [1] 0.3311147
cor(week_kuds$MaxDistReceivers, week_kuds$KUD95)
## [1] 0.1316282

KUD50

#Correlation between response and numeric explanatory variables
plot(week_kuds$LengthStd, week_kuds$KUD50, xlab = "Length Std", ylab="KUD50", col="green2", main = "KUD50 as a function of LengthStd")

plot(week_kuds$BodyMassStd, week_kuds$KUD50, xlab = "Body Mass Std", ylab="KUD50", col="green2", main = "KUD50 as a function of Body Mass Std")

plot(week_kuds$Longevity, week_kuds$KUD50, xlab = "Longevity", ylab="KUD50", col="green2", main = "KUD50 as a function of Longevity")

plot(week_kuds$Vulnerability, week_kuds$KUD50, xlab = "Vulnerability", ylab="KUD50", col="green2", main = "KUD50 as a function of Vulnerability")

plot(week_kuds$Troph, week_kuds$KUD50, xlab = "Troph", ylab="KUD50", col="green2", main = "KUD50 as a function of Troph")

plot(week_kuds$ReceiverDensity, week_kuds$KUD50, xlab = "Receiver Density", ylab="KUD50", col="green2", main = "KUD50 as a function of Receiver Density")

plot(week_kuds$MonitArea_km2, week_kuds$KUD50, xlab = "MonitArea (km2)", ylab="KUD50", col="green2", main = "KUD50 as a function of Monitored Area")

plot(week_kuds$MCP_km2, week_kuds$KUD50, xlab = "MCP (km2)", ylab="KUD50", col="green2", main = "KUD50 as a function of MCP")

plot(week_kuds$NReceivers, week_kuds$KUD50, xlab = "N Receivers", ylab="KUD50", col="green2", main = "KUD50 as a function of N Receivers")

plot(week_kuds$MaxDistReceivers, week_kuds$KUD50, xlab = "Max. Dist. Receivers", ylab="KUD50", col="green2", main = "KUD50 as a function of Max. Dist. Receivers")

cor(week_kuds$LengthStd, week_kuds$KUD50)
## [1] -0.01242211
cor(week_kuds$BodyMassStd, week_kuds$KUD50)
## [1] -0.04518465
cor(week_kuds$Longevity, week_kuds$KUD50)
## [1] -0.09251479
cor(week_kuds$Vulnerability, week_kuds$KUD50)
## [1] -0.08368454
cor(week_kuds$Troph, week_kuds$KUD50)
## [1] 0.03501519
cor(week_kuds$ReceiverDensity, week_kuds$KUD50)
## [1] -0.1059148
cor(week_kuds$MonitArea_km2, week_kuds$KUD50)
## [1] 0.313404
cor(week_kuds$MCP_km2, week_kuds$KUD50)
## [1] 0.06406506
cor(week_kuds$NReceivers, week_kuds$KUD50)
## [1] 0.3287046
cor(week_kuds$MaxDistReceivers, week_kuds$KUD50)
## [1] 0.1082455
cor.data <- data.frame(week_kuds$KUD95, week_kuds$KUD50, week_kuds$LengthStd, week_kuds$BodyMassStd, week_kuds$Longevity, week_kuds$Vulnerability, week_kuds$Troph, week_kuds$ReceiverDensity, week_kuds$MonitArea_km2, week_kuds$MCP_km2, week_kuds$NReceivers, week_kuds$MaxDistReceivers)
names(cor.data) <- c("KUD95", "KUD50", "LengthStd", "BodyMassStd", "Longevity", "Vulnerability", "Troph", "ReceiverDensity", "MonitArea_km2", "MCP_km2", "NReceivers", "MaxDistReceivers")
cortable <- cor(cor.data)
corrplot(cortable, tl.col = "black")

It doesn’t seem to exist a linear relationship between the response variables and any of the numeric explanatory variables, since the correlation is approximately 0.

To see if makes sense to introduce a certain categorical variable in a linear model, we first need to observe if the different categories of each variable are easily distinguished by their response variable values.

#Boxplots of categorical explanatory variables KUD95

par(mfrow = c(2, 3))
boxplot(KUD95 ~ Habitat, data= week_kuds, col="deepskyblue", xlab="Habitat")
title("Boxplot of KUD95 given Habitat")
boxplot(KUD95 ~ Migration, data= week_kuds, col="deepskyblue", xlab = "Migration") 
title("Boxplot of KUD95 given Migration")
boxplot(KUD95 ~ ComImport, data= week_kuds,col="deepskyblue", xlab = "Commercial Importance") 
title("Boxplot of KUD95 given Commercial Importance")

leveneTest(KUD95 ~ Habitat, data = week_kuds)
## Levene's Test for Homogeneity of Variance (center = median)
##          Df F value    Pr(>F)    
## group     2   450.7 < 2.2e-16 ***
##       25609                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
leveneTest(KUD95 ~ Migration, data = week_kuds)
## Levene's Test for Homogeneity of Variance (center = median)
##          Df F value    Pr(>F)    
## group     1  696.23 < 2.2e-16 ***
##       25610                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
leveneTest(KUD95 ~ ComImport, data = week_kuds)
## Levene's Test for Homogeneity of Variance (center = median)
##          Df F value    Pr(>F)    
## group     2  16.189 9.412e-08 ***
##       25609                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Boxplots of categorical explanatory variables KUD50

par(mfrow = c(1, 3))

boxplot(KUD50 ~ Habitat, data= week_kuds, col="green2", xlab="Habitat")
title("Boxplot of KUD50 given Habitat")
boxplot(KUD50 ~ Migration, data= week_kuds, col="green2", xlab = "Migration") 
title("Boxplot of KUD50 given Migration")
boxplot(KUD50 ~ ComImport, data= week_kuds,col="green2", xlab = "Commercial Importance") 
title("Boxplot of KUD50 given Commercial Importance")

leveneTest(KUD50 ~ Habitat, data = week_kuds)
## Levene's Test for Homogeneity of Variance (center = median)
##          Df F value    Pr(>F)    
## group     2  283.08 < 2.2e-16 ***
##       25609                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
leveneTest(KUD50 ~ Migration, data = week_kuds)
## Levene's Test for Homogeneity of Variance (center = median)
##          Df F value    Pr(>F)    
## group     1  402.53 < 2.2e-16 ***
##       25610                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
leveneTest(KUD50 ~ ComImport, data = week_kuds)
## Levene's Test for Homogeneity of Variance (center = median)
##          Df F value    Pr(>F)    
## group     2  9.7676 5.749e-05 ***
##       25609                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
kruskal.test(KUD95 ~ Habitat, data = week_kuds)
## 
##  Kruskal-Wallis rank sum test
## 
## data:  KUD95 by Habitat
## Kruskal-Wallis chi-squared = 722.95, df = 2, p-value < 2.2e-16
kruskal.test(KUD95 ~ Migration, data = week_kuds)
## 
##  Kruskal-Wallis rank sum test
## 
## data:  KUD95 by Migration
## Kruskal-Wallis chi-squared = 36.453, df = 1, p-value = 1.564e-09
kruskal.test(KUD95 ~ ComImport, data = week_kuds)
## 
##  Kruskal-Wallis rank sum test
## 
## data:  KUD95 by ComImport
## Kruskal-Wallis chi-squared = 327, df = 2, p-value < 2.2e-16

We made boxplots to observe if the values of each category were different from each other, but the only thing we could see was that there was overlapping in the values of the boxes and a lot of outliers. Also, through the levene test we could observe that the variance between groups in each categorical variable is heterogeneous.

The data violates the Linearity Assumption.

Correlation Assumption

#Biological traits correlation
cor(week_kuds$LengthStd, week_kuds$BodyMassStd)
## [1] 0.7489714
cor(week_kuds$LengthStd, week_kuds$Longevity)
## [1] -0.1539612
cor(week_kuds$LengthStd, week_kuds$Vulnerability)
## [1] -0.242292
cor(week_kuds$LengthStd, week_kuds$Troph)
## [1] -0.3042097
cor(week_kuds$BodyMassStd, week_kuds$Longevity)
## [1] -0.1734132
cor(week_kuds$BodyMassStd, week_kuds$Vulnerability)
## [1] -0.3117613
cor(week_kuds$BodyMassStd, week_kuds$Troph)
## [1] -0.1870678
cor(week_kuds$Longevity, week_kuds$Vulnerability)
## [1] 0.3942677
cor(week_kuds$Longevity, week_kuds$Troph)
## [1] 0.2072966
cor(week_kuds$Vulnerability, week_kuds$Troph)
## [1] 0.5659924
plot(correlation(week_kuds[,c(11, 13, 14, 15, 16)], method = "pearson"), main="Linear correlation between biological traits")

cor.matrix1 <- data.frame(week_kuds$LengthStd, week_kuds$BodyMassStd, week_kuds$Longevity, week_kuds$Vulnerability, week_kuds$Troph)
names(cor.matrix1) <- c("LengthStd", "BodyMassStd", "Longevity", "Vulnerability", "Troph")
cortable1 <- cor(cor.matrix1)
corrplot(cortable1, tl.col = "black")

#Experimental design parameters correlation
cor(week_kuds$ReceiverDensity, week_kuds$MonitArea_km2)
## [1] -0.3835498
cor(week_kuds$ReceiverDensity, week_kuds$MCP_km2)
## [1] -0.2094113
cor(week_kuds$ReceiverDensity, week_kuds$NReceivers)
## [1] -0.2748357
cor(week_kuds$ReceiverDensity, week_kuds$MaxDistReceivers)
## [1] -0.4524428
cor(week_kuds$MonitArea_km2, week_kuds$MCP_km2)
## [1] 0.4583335
cor(week_kuds$MonitArea_km2, week_kuds$NReceivers)
## [1] 0.9845062
cor(week_kuds$MonitArea_km2, week_kuds$MaxDistReceivers)
## [1] 0.7488135
cor(week_kuds$MCP_km2, week_kuds$NReceivers)
## [1] 0.4697432
cor(week_kuds$MCP_km2, week_kuds$MaxDistReceivers)
## [1] 0.5886984
cor(week_kuds$NReceivers, week_kuds$MaxDistReceivers)
## [1] 0.6589268
plot(correlation(week_kuds[,c(17, 18, 19, 20, 21)]), main="Linear correlation between experimental design parameters")

cor.data2 <- data.frame(week_kuds$ReceiverDensity, week_kuds$MonitArea_km2, week_kuds$MCP_km2, week_kuds$NReceivers, week_kuds$MaxDistReceivers)
names(cor.data2) <- c("ReceiverDensity", "MonitArea_km2", "MCP_km2", "NReceivers", "MaxDistReceivers")
cortable2 <- cor(cor.data2)
corrplot(cortable2, tl.col = "black")

We can observe that the only biological traits with a relative high linear correlation are the length standardised (LenghtStd) with the body mass standardised (BodyMassStd), with 74,7%. For the experimental design parameters, the monitored area has a relative high linear correlation with the number of receivers (NReceivers) and with the maximum distance between receivers (MaxDistReceivers), with 98,4% and 74,4% respectively. In future possible linear model analysis we may choose to include only one of these variables to avoid errors in coefficient estimates.

Data Transformation

To the Response Variables

Log KUD95

plot(week_kuds$LengthStd, log(week_kuds$KUD95))

plot(week_kuds$BodyMassStd, log(week_kuds$KUD95))

plot(week_kuds$Longevity, log(week_kuds$KUD95))

plot(week_kuds$Vulnerability, log(week_kuds$KUD95))

plot(week_kuds$Troph, log(week_kuds$KUD95))

cor(week_kuds$LengthStd, log(week_kuds$KUD95))
## [1] -0.0286699
cor(week_kuds$BodyMass, log(week_kuds$KUD95))
## [1] -0.06813146
cor(week_kuds$Longevity, log(week_kuds$KUD95))
## [1] -0.1182926
cor(week_kuds$Vulnerability, log(week_kuds$KUD95))
## [1] -0.08121546
cor(week_kuds$Troph, log(week_kuds$KUD95))
## [1] 0.05143071
cor(week_kuds$ReceiverDensity, log(week_kuds$KUD95))
## [1] -0.1443662
cor(week_kuds$MonitArea_km2, log(week_kuds$KUD95))
## [1] 0.3458998
cor(week_kuds$MCP_km2, log(week_kuds$KUD95))
## [1] 0.09336417
cor(week_kuds$NReceivers, log(week_kuds$KUD95))
## [1] 0.3627379
cor(week_kuds$MaxDistReceivers, log(week_kuds$KUD95))
## [1] 0.118242
hist(log(week_kuds$KUD95))

qqnorm(log(week_kuds$KUD95))
qqline(log(week_kuds$KUD95), col = 2)

Log KUD50

plot(week_kuds$LengthStd, log(week_kuds$KUD50))

plot(week_kuds$BodyMassStd, log(week_kuds$KUD50))

plot(week_kuds$Longevity, log(week_kuds$KUD50))

plot(week_kuds$Vulnerability, log(week_kuds$KUD50))

plot(week_kuds$Troph, log(week_kuds$KUD50))

cor(week_kuds$LengthStd, log(week_kuds$KUD50))
## [1] -0.0111613
cor(week_kuds$BodyMass, log(week_kuds$KUD50))
## [1] -0.07993413
cor(week_kuds$Longevity, log(week_kuds$KUD50))
## [1] -0.1187778
cor(week_kuds$Vulnerability, log(week_kuds$KUD50))
## [1] -0.1100756
cor(week_kuds$Troph, log(week_kuds$KUD50))
## [1] 0.0208178
cor(week_kuds$ReceiverDensity, log(week_kuds$KUD50))
## [1] -0.1158277
cor(week_kuds$MonitArea_km2, log(week_kuds$KUD50))
## [1] 0.3294109
cor(week_kuds$MCP_km2, log(week_kuds$KUD50))
## [1] 0.06668169
cor(week_kuds$NReceivers, log(week_kuds$KUD50))
## [1] 0.3509882
cor(week_kuds$MaxDistReceivers, log(week_kuds$KUD50))
## [1] 0.09256047
hist(log(week_kuds$KUD50))

qqnorm(log(week_kuds$KUD50))
qqline(log(week_kuds$KUD50), col = 2)

The log transformation doesn’t resolve the problem of the Linearity Assumption.

x^2 KUD95

plot(week_kuds$LengthStd, (week_kuds$KUD95)^2)

plot(week_kuds$BodyMassStd, (week_kuds$KUD95)^2)

plot(week_kuds$Longevity, (week_kuds$KUD95)^2)

plot(week_kuds$Vulnerability, (week_kuds$KUD95)^2)

plot(week_kuds$Troph, (week_kuds$KUD95)^2)

cor(week_kuds$LengthStd, (week_kuds$KUD95)^2)
## [1] -0.005002298
cor(week_kuds$BodyMassStd, (week_kuds$KUD95)^2)
## [1] -0.03391904
cor(week_kuds$Longevity, (week_kuds$KUD95)^2)
## [1] -0.04002724
cor(week_kuds$Vulnerability, (week_kuds$KUD95)^2)
## [1] -0.01940918
cor(week_kuds$Troph, (week_kuds$KUD95)^2)
## [1] 0.04631723
cor(week_kuds$ReceiverDensity, (week_kuds$KUD95)^2)
## [1] -0.0749169
cor(week_kuds$MonitArea_km2, (week_kuds$KUD95)^2)
## [1] 0.2242074
cor(week_kuds$MCP_km2, (week_kuds$KUD95)^2)
## [1] 0.04771912
cor(week_kuds$NReceivers, (week_kuds$KUD95)^2)
## [1] 0.2287884
cor(week_kuds$MaxDistReceivers, (week_kuds$KUD95)^2)
## [1] 0.1022885
hist((week_kuds$KUD95)^2)

qqnorm((week_kuds$KUD95)^2)
qqline((week_kuds$KUD95)^2, col = 2)

x^2 KUD50

plot(week_kuds$LengthStd, (week_kuds$KUD50)^2)

plot(week_kuds$BodyMassStd, (week_kuds$KUD50)^2)

plot(week_kuds$Longevity, (week_kuds$KUD50)^2)

plot(week_kuds$Vulnerability, (week_kuds$KUD50)^2)

plot(week_kuds$Troph, (week_kuds$KUD50)^2)

cor(week_kuds$LengthStd, (week_kuds$KUD50)^2)
## [1] -0.003901381
cor(week_kuds$BodyMassStd, (week_kuds$KUD50)^2)
## [1] -0.03280761
cor(week_kuds$Longevity, (week_kuds$KUD50)^2)
## [1] -0.05070559
cor(week_kuds$Vulnerability, (week_kuds$KUD50)^2)
## [1] -0.04537786
cor(week_kuds$Troph, (week_kuds$KUD50)^2)
## [1] 0.0283501
cor(week_kuds$ReceiverDensity, (week_kuds$KUD50)^2)
## [1] -0.06603929
cor(week_kuds$MonitArea_km2, (week_kuds$KUD50)^2)
## [1] 0.2099815
cor(week_kuds$MCP_km2, (week_kuds$KUD50)^2)
## [1] 0.04016722
cor(week_kuds$NReceivers, (week_kuds$KUD50)^2)
## [1] 0.2173508
cor(week_kuds$MaxDistReceivers, (week_kuds$KUD50)^2)
## [1] 0.08308354
hist((week_kuds$KUD50)^2)

qqnorm((week_kuds$KUD50)^2)
qqline((week_kuds$KUD50)^2, col = 2)

The sqaured transformation doesn’t resolve the problem of the Linearity Assumption.

Squared root KUD95

plot(week_kuds$LengthStd, sqrt(week_kuds$KUD95))

plot(week_kuds$BodyMassStd, sqrt(week_kuds$KUD95))

plot(week_kuds$Longevity, sqrt(week_kuds$KUD95))

plot(week_kuds$Vulnerability, sqrt(week_kuds$KUD95))

plot(week_kuds$Troph, sqrt(week_kuds$KUD95))

cor(week_kuds$LengthStd, sqrt(week_kuds$KUD95))
## [1] -0.02667786
cor(week_kuds$BodyMass, sqrt(week_kuds$KUD95))
## [1] -0.05735046
cor(week_kuds$Longevity, sqrt(week_kuds$KUD95))
## [1] -0.1018989
cor(week_kuds$Vulnerability, sqrt(week_kuds$KUD95))
## [1] -0.06670016
cor(week_kuds$Troph, sqrt(week_kuds$KUD95))
## [1] 0.0588293
cor(week_kuds$ReceiverDensity, sqrt(week_kuds$KUD95))
## [1] -0.1374409
cor(week_kuds$MonitArea_km2, sqrt(week_kuds$KUD95))
## [1] 0.343724
cor(week_kuds$MCP_km2, sqrt(week_kuds$KUD95))
## [1] 0.09047568
cor(week_kuds$NReceivers, sqrt(week_kuds$KUD95))
## [1] 0.3575089
cor(week_kuds$MaxDistReceivers, sqrt(week_kuds$KUD95))
## [1] 0.1298021
hist(sqrt(week_kuds$KUD95))

qqnorm(sqrt(week_kuds$KUD95))
qqline(sqrt(week_kuds$KUD95), col = 2)

Squared root KUD50

plot(week_kuds$LengthStd, sqrt(week_kuds$KUD50))

plot(week_kuds$BodyMassStd, sqrt(week_kuds$KUD50))

plot(week_kuds$Longevity, sqrt(week_kuds$KUD50))

plot(week_kuds$Vulnerability, sqrt(week_kuds$KUD50))

plot(week_kuds$Troph, sqrt(week_kuds$KUD50))

cor(week_kuds$LengthStd, sqrt(week_kuds$KUD50))
## [1] -0.01254113
cor(week_kuds$BodyMass, sqrt(week_kuds$KUD50))
## [1] -0.07007703
cor(week_kuds$Longevity, sqrt(week_kuds$KUD50))
## [1] -0.107971
cor(week_kuds$Vulnerability, sqrt(week_kuds$KUD50))
## [1] -0.09901242
cor(week_kuds$Troph, sqrt(week_kuds$KUD50))
## [1] 0.0292903
cor(week_kuds$ReceiverDensity, sqrt(week_kuds$KUD50))
## [1] -0.1141593
cor(week_kuds$MonitArea_km2, sqrt(week_kuds$KUD50))
## [1] 0.3296213
cor(week_kuds$MCP_km2, sqrt(week_kuds$KUD50))
## [1] 0.06758948
cor(week_kuds$NReceivers, sqrt(week_kuds$KUD50))
## [1] 0.3484063
cor(week_kuds$MaxDistReceivers, sqrt(week_kuds$KUD50))
## [1] 0.1035089
hist(sqrt(week_kuds$KUD50))

qqnorm(sqrt(week_kuds$KUD50))
qqline(sqrt(week_kuds$KUD50), col = 2)

The squared root transformation doesn’t resolve the problem of the Linearity Assumption.

Inverse KUD95

plot(week_kuds$LengthStd, 1/week_kuds$KUD95)

plot(week_kuds$BodyMassStd, 1/week_kuds$KUD95)

plot(week_kuds$Longevity, 1/week_kuds$KUD95)

plot(week_kuds$Vulnerability, 1/week_kuds$KUD95)

plot(week_kuds$Troph, 1/week_kuds$KUD95)

cor(week_kuds$LengthStd, 1/week_kuds$KUD95)
## [1] 0.02746866
cor(week_kuds$BodyMass, 1/week_kuds$KUD95)
## [1] 0.0836109
cor(week_kuds$Longevity, 1/week_kuds$KUD95)
## [1] 0.1362053
cor(week_kuds$Vulnerability, 1/week_kuds$KUD95)
## [1] 0.09961442
cor(week_kuds$Troph, 1/week_kuds$KUD95)
## [1] -0.0311552
cor(week_kuds$ReceiverDensity, 1/(week_kuds$KUD95))
## [1] 0.1407617
cor(week_kuds$MonitArea_km2, 1/(week_kuds$KUD95))
## [1] -0.321462
cor(week_kuds$MCP_km2, 1/(week_kuds$KUD95))
## [1] -0.08628076
cor(week_kuds$NReceivers, 1/(week_kuds$KUD95))
## [1] -0.3427128
cor(week_kuds$MaxDistReceivers, 1/(week_kuds$KUD95))
## [1] -0.08664873
hist(1/week_kuds$KUD95)

qqnorm(1/week_kuds$KUD95)
qqline(1/week_kuds$KUD95, col = 2)

Inverse KUD50

plot(week_kuds$LengthStd, 1/week_kuds$KUD50)

plot(week_kuds$BodyMassStd, 1/week_kuds$KUD50)

plot(week_kuds$Longevity, 1/week_kuds$KUD50)

plot(week_kuds$Vulnerability, 1/week_kuds$KUD50)

plot(week_kuds$Troph, 1/week_kuds$KUD50)

cor(week_kuds$LengthStd, 1/week_kuds$KUD50)
## [1] 0.007983447
cor(week_kuds$BodyMass, 1/week_kuds$KUD50)
## [1] 0.09435985
cor(week_kuds$Longevity, 1/week_kuds$KUD50)
## [1] 0.1309154
cor(week_kuds$Vulnerability, 1/week_kuds$KUD50)
## [1] 0.1230782
cor(week_kuds$Troph, 1/week_kuds$KUD50)
## [1] -0.003295783
cor(week_kuds$ReceiverDensity, 1/(week_kuds$KUD50))
## [1] 0.1093812
cor(week_kuds$MonitArea_km2, 1/(week_kuds$KUD50))
## [1] -0.3103144
cor(week_kuds$MCP_km2, 1/(week_kuds$KUD50))
## [1] -0.05909859
cor(week_kuds$NReceivers, 1/(week_kuds$KUD50))
## [1] -0.3358942
cor(week_kuds$MaxDistReceivers, 1/(week_kuds$KUD50))
## [1] -0.06677873
hist(1/week_kuds$KUD50)

qqnorm(1/week_kuds$KUD50)
qqline(1/week_kuds$KUD50, col = 2)

The inverse transformation doesn’t resolve the problem of the Linearity Assumption.

Transformations to the response variables were made to try to meet the assumption of linearity and normality. However, we hadn’t any positive results and the data kept violating the assumptions.

To the Explanatory Variables

#KUD95

cor(log(week_kuds$KUD95), log(week_kuds$LengthStd))
## [1] -0.01835412
cor(log(week_kuds$KUD95), log(week_kuds$BodyMassStd))
## [1] -0.04289739
cor(log(week_kuds$KUD95),log(week_kuds$Longevity))
## [1] -0.08026167
cor(log(week_kuds$KUD95),log(week_kuds$Vulnerability))
## [1] -0.07351917
cor(log(week_kuds$KUD95),log(week_kuds$Troph))
## [1] 0.04695362
cor(log(week_kuds$ReceiverDensity), week_kuds$KUD95)
## [1] -0.1164072
cor(log(week_kuds$MonitArea_km2), week_kuds$KUD95)
## [1] 0.2540448
cor(log(week_kuds$MCP_km2), week_kuds$KUD95)
## [1] 0.1903855
cor(log(week_kuds$NReceivers), week_kuds$KUD95)
## [1] 0.2427251
cor(log(week_kuds$MaxDistReceivers), week_kuds$KUD95)
## [1] 0.1725921
cor(week_kuds$KUD50, log(week_kuds$LengthStd))
## [1] -0.00651373
cor(week_kuds$KUD50, log(week_kuds$BodyMassStd))
## [1] -0.03474324
cor(week_kuds$KUD50,log(week_kuds$Longevity))
## [1] -0.06410422
cor(week_kuds$KUD50,log(week_kuds$Vulnerability))
## [1] -0.07578021
cor(week_kuds$KUD50,log(week_kuds$Troph))
## [1] 0.03158416
cor(log(week_kuds$ReceiverDensity), week_kuds$KUD50)
## [1] -0.07684298
cor(log(week_kuds$MonitArea_km2), week_kuds$KUD50)
## [1] 0.2564476
cor(log(week_kuds$MCP_km2), week_kuds$KUD50)
## [1] 0.1618219
cor(log(week_kuds$NReceivers), week_kuds$KUD50)
## [1] 0.2544955
cor(log(week_kuds$MaxDistReceivers), week_kuds$KUD50)
## [1] 0.144812
cor(week_kuds$KUD95, week_kuds$LengthStd^2)
## [1] -0.03205761
cor(week_kuds$KUD95, week_kuds$BodyMassStd^2)
## [1] -0.04682318
cor(week_kuds$KUD95, week_kuds$Longevity^2)
## [1] -0.09960863
cor(week_kuds$KUD95, week_kuds$Vulnerability^2)
## [1] -0.05235909
cor(week_kuds$KUD95, week_kuds$Troph^2)
## [1] 0.0654842
cor(week_kuds$ReceiverDensity^2, week_kuds$KUD95)
## [1] -0.08336138
cor(week_kuds$MonitArea_km2^2, week_kuds$KUD95)
## [1] 0.3544305
cor(week_kuds$MCP_km2^2, week_kuds$KUD95)
## [1] 0.0229182
cor(week_kuds$NReceivers^2, week_kuds$KUD95)
## [1] 0.3591757
cor(week_kuds$MaxDistReceivers^2, week_kuds$KUD95)
## [1] 0.07860331
cor(week_kuds$KUD50, week_kuds$LengthStd^2)
## [1] -0.01903804
cor(week_kuds$KUD50, week_kuds$BodyMassStd^2)
## [1] -0.03461579
cor(week_kuds$KUD50, week_kuds$Longevity^2)
## [1] -0.1127521
cor(week_kuds$KUD50, week_kuds$Vulnerability^2)
## [1] -0.08910187
cor(week_kuds$KUD50, week_kuds$Troph^2)
## [1] 0.03937493
cor(week_kuds$ReceiverDensity^2, week_kuds$KUD50)
## [1] -0.08135174
cor(week_kuds$MonitArea_km2^2, week_kuds$KUD50)
## [1] 0.3468013
cor(week_kuds$MCP_km2^2, week_kuds$KUD50)
## [1] 0.01081609
cor(week_kuds$NReceivers^2, week_kuds$KUD50)
## [1] 0.353728
cor(week_kuds$MaxDistReceivers^2, week_kuds$KUD50)
## [1] 0.05578804
cor(week_kuds$KUD95, sqrt(week_kuds$LengthStd))
## [1] -0.01508509
cor(week_kuds$KUD95, sqrt(week_kuds$BodyMassStd))
## [1] -0.04897952
cor(week_kuds$KUD95, sqrt(week_kuds$Longevity))
## [1] -0.06806335
cor(week_kuds$KUD95, sqrt(week_kuds$Vulnerability))
## [1] -0.04682171
cor(week_kuds$KUD95, sqrt(week_kuds$Troph))
## [1] 0.05879031
cor(sqrt(week_kuds$ReceiverDensity), week_kuds$KUD95)
## [1] -0.1378731
cor(sqrt(week_kuds$MonitArea_km2), week_kuds$KUD95)
## [1] 0.2912277
cor(sqrt(week_kuds$MCP_km2), week_kuds$KUD95)
## [1] 0.1567625
cor(sqrt(week_kuds$NReceivers), week_kuds$KUD95)
## [1] 0.2967655
cor(sqrt(week_kuds$MaxDistReceivers), week_kuds$KUD95)
## [1] 0.1564256
cor(week_kuds$KUD50, sqrt(week_kuds$LengthStd))
## [1] -0.009253797
cor(week_kuds$KUD50, sqrt(week_kuds$BodyMassStd))
## [1] -0.04507737
cor(week_kuds$KUD50, sqrt(week_kuds$Longevity))
## [1] -0.07897539
cor(week_kuds$KUD50, sqrt(week_kuds$Vulnerability))
## [1] -0.07999811
cor(week_kuds$KUD50, sqrt(week_kuds$Troph))
## [1] 0.03317919
cor(sqrt(week_kuds$ReceiverDensity), week_kuds$KUD50)
## [1] -0.1073464
cor(sqrt(week_kuds$MonitArea_km2), week_kuds$KUD50)
## [1] 0.2872584
cor(sqrt(week_kuds$MCP_km2), week_kuds$KUD50)
## [1] 0.1343301
cor(sqrt(week_kuds$NReceivers), week_kuds$KUD50)
## [1] 0.2999491
cor(sqrt(week_kuds$MaxDistReceivers), week_kuds$KUD50)
## [1] 0.1311348
cor(week_kuds$KUD95, 1/week_kuds$LengthStd)
## [1] -0.004437635
cor(week_kuds$KUD95, 1/week_kuds$BodyMassStd)
## [1] -0.01290895
cor(week_kuds$KUD95, 1/week_kuds$Longevity)
## [1] 0.02693019
cor(week_kuds$KUD95, 1/week_kuds$Vulnerability)
## [1] 0.0370628
cor(week_kuds$KUD95, 1/week_kuds$Troph)
## [1] -0.05382839
cor(1/week_kuds$ReceiverDensity, week_kuds$KUD95)
## [1] 0.0196011
cor(1/week_kuds$MonitArea_km2, week_kuds$KUD95)
## [1] -0.1869263
cor(1/week_kuds$MCP_km2, week_kuds$KUD95)
## [1] -0.1250668
cor(1/week_kuds$NReceivers, week_kuds$KUD95)
## [1] -0.1408403
cor(1/week_kuds$MaxDistReceivers, week_kuds$KUD95)
## [1] -0.1436929
cor(week_kuds$KUD50, 1/week_kuds$LengthStd)
## [1] 0.003311695
cor(week_kuds$KUD50, 1/week_kuds$BodyMassStd)
## [1] 0.01423426
cor(week_kuds$KUD50, 1/week_kuds$Longevity)
## [1] 0.03607618
cor(week_kuds$KUD50, 1/week_kuds$Vulnerability)
## [1] 0.06618764
cor(week_kuds$KUD50, 1/week_kuds$Troph)
## [1] -0.02913728
cor(1/week_kuds$ReceiverDensity, week_kuds$KUD50)
## [1] -0.005819776
cor(1/week_kuds$MonitArea_km2, week_kuds$KUD50)
## [1] -0.2036961
cor(1/week_kuds$MCP_km2, week_kuds$KUD50)
## [1] -0.1124298
cor(1/week_kuds$NReceivers, week_kuds$KUD50)
## [1] -0.1667151
cor(1/week_kuds$MaxDistReceivers, week_kuds$KUD50)
## [1] -0.123848

Transformations to the independent variables were made to try to meet the assumption of linearity. However, we hadn’t any positive results and the data kept violating the assumption.

Modelling KUD95

We tested the full linear model and checked the assumptions.

#Full model assumption checking
lmtotal <- lm(KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + ReceiverDensity + MonitArea_km2, data = week_kuds)
summary(lmtotal)
## 
## Call:
## lm(formula = KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + 
##     Troph + Habitat + Migration + ComImport + ReceiverDensity + 
##     MonitArea_km2, data = week_kuds)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.4379 -0.3405 -0.1232  0.1770 12.8982 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             0.2757879  0.0674894   4.086 4.39e-05 ***
## LengthStd               0.4682903  0.0550355   8.509  < 2e-16 ***
## BodyMassStd            -0.4322197  0.0474916  -9.101  < 2e-16 ***
## Longevity              -0.0061898  0.0004149 -14.918  < 2e-16 ***
## Vulnerability           0.0051100  0.0007503   6.811 9.92e-12 ***
## Troph                   0.0069510  0.0166141   0.418   0.6757    
## Habitatdemersal        -0.1200547  0.0195780  -6.132 8.80e-10 ***
## Habitatpelagic-neritic  0.3613265  0.0216434  16.695  < 2e-16 ***
## Migrationoceanodromous  0.1313238  0.0193442   6.789 1.16e-11 ***
## ComImportmedium        -0.1745732  0.0133959 -13.032  < 2e-16 ***
## ComImportminor          0.1102418  0.0523992   2.104   0.0354 *  
## ReceiverDensity         0.0040995  0.0002647  15.485  < 2e-16 ***
## MonitArea_km2           0.0652505  0.0012001  54.369  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7087 on 25599 degrees of freedom
## Multiple R-squared:  0.1895, Adjusted R-squared:  0.1891 
## F-statistic: 498.7 on 12 and 25599 DF,  p-value: < 2.2e-16
#Normality assumption
qqnorm(lmtotal$residuals)
qqline(lmtotal$residuals, col = 2)

#Homocedascity assumption
plot(lmtotal$fitted.values, lmtotal$residuals)
abline(h=0, col="red")

As expected, the linear model do not fit well to our data, since the assumptions are not met.

We tried to fit different types of models. We modeled the response against each predictor variable separately. We then compare the AIC of each model to see which one fitted better.

#Modeling (each variable separately)

#Length Standardised
lm_length <- lm(KUD95 ~ LengthStd, data=week_kuds)
glm_length <- glm(KUD95 ~ LengthStd, data=week_kuds, family=Gamma(link="log"))
gam_length <- gam(KUD95 ~ LengthStd, data=week_kuds, family=Gamma(link="log"))
glmmF_length <- glmmTMB(KUD95 ~ LengthStd + (1|File), data=week_kuds, family=Gamma(link="log"))
glmmT_length <- glmmTMB(KUD95 ~ LengthStd + (1|Transmitter), data=week_kuds, family=Gamma(link="log")) #control = glmerControl(optimizer = "bobyqa")
glmmS_length <- glmmTMB(KUD95 ~ LengthStd + (1|Species), data=week_kuds, family=Gamma(link="log"))
gammF_length <- gamm(KUD95 ~ s(LengthStd), random=list(File=~1), data= week_kuds, family=Gamma(link="log"))   #Preference for gmcv package since gamm4 uses glm methods
## 
##  Maximum number of PQL iterations:  20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
## iteration 5
#gamm4F_length <- gamm4(KUD95 ~ s(LengthStd), random = ~(1|File), data = week_kuds, family=Gamma(link="log"))
gammT_length <- gamm(KUD95 ~ s(LengthStd), random=list(Transmitter=~1), data= week_kuds, family=Gamma(link="log"))   #Preference for gmcv package since gamm4 uses glm methods
## 
##  Maximum number of PQL iterations:  20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
## iteration 5
## iteration 6
## iteration 7
#gamm4T_length <- gamm4(KUD95 ~ s(LengthStd), random = ~(1|Transmitter), data = week_kuds, family=Gamma(link="log"))
gammS_length <- gamm(KUD95 ~ s(LengthStd), random=list(Species=~1), data= week_kuds, family=Gamma(link="log"))   #Preference for gmcv package since gamm4 uses glm methods
## 
##  Maximum number of PQL iterations:  20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
## iteration 5
#gamm4S_length <- gamm4(KUD95 ~ s(LengthStd), random = ~(1|Species), data = week_kuds, family=Gamma(link="log"))


AIC(lm_length, glm_length, gam_length, glmmF_length, glmmT_length, glmmS_length)
##              df      AIC
## lm_length     3 60408.26
## glm_length    3 35354.67
## gam_length    3 42791.40
## glmmF_length  4 20408.20
## glmmT_length  4 10331.08
## glmmS_length  4 25565.58
summary(gammF_length$lme)
## Linear mixed-effects model fit by maximum likelihood
##   Data: data 
##        AIC      BIC    logLik
##   30060.72 30101.48 -15025.36
## 
## Random effects:
##  Formula: ~Xr - 1 | g
##  Structure: pdIdnot
##              Xr1      Xr2      Xr3      Xr4      Xr5      Xr6      Xr7      Xr8
## StdDev: 1.402132 1.402132 1.402132 1.402132 1.402132 1.402132 1.402132 1.402132
## 
##  Formula: ~1 | File %in% g
##         (Intercept)  Residual
## StdDev:   0.3753014 0.4326938
## 
## Variance function:
##  Structure: fixed weights
##  Formula: ~invwt 
## Fixed effects:  list(fixed) 
##                       Value  Std.Error    DF   t-value p-value
## X(Intercept)      0.1079515 0.05499357 25563  1.962984  0.0497
## Xs(LengthStd)Fx1 -0.2165399 0.05370046 25563 -4.032365  0.0001
##  Correlation: 
##                  X(Int)
## Xs(LengthStd)Fx1 -0.001
## 
## Standardized Within-Group Residuals:
##        Min         Q1        Med         Q3        Max 
## -1.7310314 -0.4877050 -0.2048337  0.1705059 17.7859651 
## 
## Number of Observations: 25612
## Number of Groups: 
##           g File %in% g 
##           1          48
summary(gammT_length$lme)
## Linear mixed-effects model fit by maximum likelihood
##   Data: data 
##        AIC      BIC    logLik
##   16441.62 16482.38 -8215.812
## 
## Random effects:
##  Formula: ~Xr - 1 | g
##  Structure: pdIdnot
##                  Xr1          Xr2          Xr3          Xr4          Xr5
## StdDev: 0.0003865896 0.0003865896 0.0003865896 0.0003865896 0.0003865896
##                  Xr6          Xr7          Xr8
## StdDev: 0.0003865896 0.0003865896 0.0003865896
## 
##  Formula: ~1 | Transmitter %in% g
##         (Intercept)  Residual
## StdDev:   0.3775188 0.3164446
## 
## Variance function:
##  Structure: fixed weights
##  Formula: ~invwt 
## Fixed effects:  list(fixed) 
##                       Value  Std.Error    DF  t-value p-value
## X(Intercept)     0.09778024 0.01355406 24761 7.214091  0.0000
## Xs(LengthStd)Fx1 0.01511383 0.01306119 24761 1.157155  0.2472
##  Correlation: 
##                  X(Int)
## Xs(LengthStd)Fx1 0.082 
## 
## Standardized Within-Group Residuals:
##         Min          Q1         Med          Q3         Max 
## -2.37337902 -0.43329724 -0.10739754  0.09135131 16.69617083 
## 
## Number of Observations: 25612
## Number of Groups: 
##                  g Transmitter %in% g 
##                  1                850
summary(gammS_length$lme)
## Linear mixed-effects model fit by maximum likelihood
##   Data: data 
##        AIC      BIC    logLik
##   38088.52 38129.27 -19039.26
## 
## Random effects:
##  Formula: ~Xr - 1 | g
##  Structure: pdIdnot
##              Xr1      Xr2      Xr3      Xr4      Xr5      Xr6      Xr7      Xr8
## StdDev: 1.867361 1.867361 1.867361 1.867361 1.867361 1.867361 1.867361 1.867361
## 
##  Formula: ~1 | Species %in% g
##         (Intercept)  Residual
## StdDev:   0.3874701 0.5069951
## 
## Variance function:
##  Structure: fixed weights
##  Formula: ~invwt 
## Fixed effects:  list(fixed) 
##                        Value  Std.Error    DF   t-value p-value
## X(Intercept)      0.14748060 0.07210376 25581  2.045394  0.0408
## Xs(LengthStd)Fx1 -0.07881835 0.06115692 25581 -1.288789  0.1975
##  Correlation: 
##                  X(Int)
## Xs(LengthStd)Fx1 0     
## 
## Standardized Within-Group Residuals:
##        Min         Q1        Med         Q3        Max 
## -1.4751826 -0.4269536 -0.1770516  0.1560576 16.7949077 
## 
## Number of Observations: 25612
## Number of Groups: 
##              g Species %in% g 
##              1             30
#BodyMass Standardised
lm_bodymass <- lm(KUD95 ~ BodyMassStd, data=week_kuds)
glm_bodymass <- glm(KUD95 ~ BodyMassStd, data=week_kuds, family=Gamma(link="log"))
gam_bodymass <- gam(KUD95 ~ BodyMassStd, data=week_kuds, family=Gamma(link="log"))
glmmF_bodymass <- glmmTMB(KUD95 ~ BodyMassStd + (1|File), data=week_kuds, family=Gamma(link="log"))
glmmT_bodymass <- glmmTMB(KUD95 ~ BodyMassStd + (1|Transmitter), data=week_kuds, family=Gamma(link="log"))
glmmS_bodymass <- glmmTMB(KUD95 ~ BodyMassStd + (1|Species), data=week_kuds, family=Gamma(link="log"))
gammF_bodymass <- gamm(KUD95 ~ s(BodyMassStd), random=list(File=~1), data= week_kuds, family=Gamma(link="log"))   #Preference for gmcv package since gamm4 uses glm methods
## 
##  Maximum number of PQL iterations:  20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
## iteration 5
#gamm4F_bodymass <- gamm4(KUD95 ~ s(BodyMassStd), random = ~(1|File), data = week_kuds, family=Gamma(link="log"))
gammT_bodymass <- gamm(KUD95 ~ s(BodyMassStd), random=list(Transmitter=~1), data= week_kuds, family=Gamma(link="log"))   #Preference for gmcv package since gamm4 uses glm methods
## 
##  Maximum number of PQL iterations:  20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
## iteration 5
## iteration 6
## iteration 7
#gamm4T_bodymass <- gamm4(KUD95 ~ s(BodyMassStd), random = ~(1|Transmitter), data = week_kuds, family=Gamma(link="log"))
gammS_bodymass <- gamm(KUD95 ~ s(BodyMassStd), random=list(Species=~1), data= week_kuds, family=Gamma(link="log"))   #Preference for gmcv package since gamm4 uses glm methods
## 
##  Maximum number of PQL iterations:  20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
## iteration 5
#gamm4S_bodymass <- gamm4(KUD95 ~ s(BodyMassStd), random = ~(1|Species), data = week_kuds, family=Gamma(link="log"))


AIC(lm_bodymass, glm_bodymass, gam_bodymass, glmmF_bodymass, glmmT_bodymass, glmmS_bodymass)
##                df      AIC
## lm_bodymass     3 60341.62
## glm_bodymass    3 35194.80
## gam_bodymass    3 42506.22
## glmmF_bodymass  4 20457.95
## glmmT_bodymass  4 10330.63
## glmmS_bodymass  4 25623.81
summary(gammF_bodymass$lme)
## Linear mixed-effects model fit by maximum likelihood
##   Data: data 
##        AIC      BIC    logLik
##   30157.54 30198.29 -15073.77
## 
## Random effects:
##  Formula: ~Xr - 1 | g
##  Structure: pdIdnot
##              Xr1      Xr2      Xr3      Xr4      Xr5      Xr6      Xr7      Xr8
## StdDev: 1.620637 1.620637 1.620637 1.620637 1.620637 1.620637 1.620637 1.620637
## 
##  Formula: ~1 | File %in% g
##         (Intercept)  Residual
## StdDev:   0.3736541 0.4334934
## 
## Variance function:
##  Structure: fixed weights
##  Formula: ~invwt 
## Fixed effects:  list(fixed) 
##                         Value  Std.Error    DF   t-value p-value
## X(Intercept)        0.1045715 0.05476946 25563  1.909303  0.0562
## Xs(BodyMassStd)Fx1 -0.2814991 0.09460800 25563 -2.975426  0.0029
##  Correlation: 
##                    X(Int)
## Xs(BodyMassStd)Fx1 0.006 
## 
## Standardized Within-Group Residuals:
##        Min         Q1        Med         Q3        Max 
## -1.7324494 -0.4964458 -0.2160497  0.1688127 18.1774451 
## 
## Number of Observations: 25612
## Number of Groups: 
##           g File %in% g 
##           1          48
summary(gammT_bodymass$lme)
## Linear mixed-effects model fit by maximum likelihood
##   Data: data 
##        AIC      BIC    logLik
##   16438.86 16479.61 -8214.429
## 
## Random effects:
##  Formula: ~Xr - 1 | g
##  Structure: pdIdnot
##                  Xr1          Xr2          Xr3          Xr4          Xr5
## StdDev: 0.0002035433 0.0002035433 0.0002035433 0.0002035433 0.0002035433
##                  Xr6          Xr7          Xr8
## StdDev: 0.0002035433 0.0002035433 0.0002035433
## 
##  Formula: ~1 | Transmitter %in% g
##         (Intercept)  Residual
## StdDev:   0.3762537 0.3164604
## 
## Variance function:
##  Structure: fixed weights
##  Formula: ~invwt 
## Fixed effects:  list(fixed) 
##                         Value   Std.Error    DF  t-value p-value
## X(Intercept)       0.09498825 0.013493890 24761 7.039353  0.0000
## Xs(BodyMassStd)Fx1 0.01629013 0.009569512 24761 1.702295  0.0887
##  Correlation: 
##                    X(Int)
## Xs(BodyMassStd)Fx1 -0.064
## 
## Standardized Within-Group Residuals:
##         Min          Q1         Med          Q3         Max 
## -2.37340997 -0.43360867 -0.10700779  0.09153392 16.68484562 
## 
## Number of Observations: 25612
## Number of Groups: 
##                  g Transmitter %in% g 
##                  1                850
summary(gammS_bodymass$lme)
## Linear mixed-effects model fit by maximum likelihood
##   Data: data 
##        AIC      BIC    logLik
##   38618.86 38659.61 -19304.43
## 
## Random effects:
##  Formula: ~Xr - 1 | g
##  Structure: pdIdnot
##              Xr1      Xr2      Xr3      Xr4      Xr5      Xr6      Xr7      Xr8
## StdDev: 1.892624 1.892624 1.892624 1.892624 1.892624 1.892624 1.892624 1.892624
## 
##  Formula: ~1 | Species %in% g
##         (Intercept)  Residual
## StdDev:   0.3845178 0.5122769
## 
## Variance function:
##  Structure: fixed weights
##  Formula: ~invwt 
## Fixed effects:  list(fixed) 
##                         Value  Std.Error    DF   t-value p-value
## X(Intercept)        0.1506280 0.07163738 25581  2.102645  0.0355
## Xs(BodyMassStd)Fx1 -0.3744871 0.11116730 25581 -3.368681  0.0008
##  Correlation: 
##                    X(Int)
## Xs(BodyMassStd)Fx1 0.008 
## 
## Standardized Within-Group Residuals:
##        Min         Q1        Med         Q3        Max 
## -1.4710260 -0.4378846 -0.1981203  0.1411153 16.2151776 
## 
## Number of Observations: 25612
## Number of Groups: 
##              g Species %in% g 
##              1             30
#Longevity
lm_longevity <- lm(KUD95 ~ Longevity, data=week_kuds)
glm_longevity <- glm(KUD95 ~ Longevity, data=week_kuds, family=Gamma(link="log"))
gam_longevity <- gam(KUD95 ~ Longevity, data=week_kuds, family=Gamma(link="log"))
glmmF_longevity <- glmmTMB(KUD95 ~ Longevity + (1|File), data=week_kuds, family=Gamma(link="log"))
glmmT_longevity <- glmmTMB(KUD95 ~ Longevity + (1|Transmitter), data=week_kuds, family=Gamma(link="log"))
glmmS_longevity <- glmmTMB(KUD95 ~ Longevity + (1|Species), data=week_kuds, family=Gamma(link="log"))
gammF_longevity <- gamm(KUD95 ~ s(Longevity), random=list(File=~1), data= week_kuds, family=Gamma(link="log"))   #Preference for gmcv package since gamm4 uses glm methods
## 
##  Maximum number of PQL iterations:  20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
#gamm4F_longevity <- gamm4(KUD95 ~ s(Longevity), random = ~(1|File), data = week_kuds, family=Gamma(link="log"))
gammT_longevity <- gamm(KUD95 ~ s(Longevity), random=list(Transmitter=~1), data= week_kuds, family=Gamma(link="log"))   #Preference for gmcv package since gamm4 uses glm methods
## 
##  Maximum number of PQL iterations:  20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
## iteration 5
## iteration 6
## iteration 7
#gamm4T_longevity <- gamm4(KUD95 ~ s(Longevity), random = ~(1|Transmitter), data = week_kuds, family=Gamma(link="log"))
gammS_longevity <- gamm(KUD95 ~ s(Longevity), random=list(Species=~1), data= week_kuds, family=Gamma(link="log"))   #Preference for gmcv package since gamm4 uses glm methods
## 
##  Maximum number of PQL iterations:  20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
#gamm4S_longevity <- gamm4(KUD95 ~ s(Longevity), random = ~(1|Species), data = week_kuds, family=Gamma(link="log"))

AIC(lm_longevity, glm_longevity, gam_longevity, glmmF_longevity, glmmT_longevity, glmmS_longevity)
##                 df      AIC
## lm_longevity     3 60252.94
## glm_longevity    3 34951.62
## gam_longevity    3 42313.90
## glmmF_longevity  4 20491.38
## glmmT_longevity  4 10325.84
## glmmS_longevity  4 25626.29
summary(gammF_longevity$lme)
## Linear mixed-effects model fit by maximum likelihood
##   Data: data 
##        AIC      BIC    logLik
##   30657.31 30698.06 -15323.65
## 
## Random effects:
##  Formula: ~Xr - 1 | g
##  Structure: pdIdnot
##                 Xr1         Xr2         Xr3         Xr4         Xr5         Xr6
## StdDev: 0.004994435 0.004994435 0.004994435 0.004994435 0.004994435 0.004994435
##                 Xr7         Xr8
## StdDev: 0.004994435 0.004994435
## 
##  Formula: ~1 | File %in% g
##         (Intercept)  Residual
## StdDev:   0.3628811 0.4381039
## 
## Variance function:
##  Structure: fixed weights
##  Formula: ~invwt 
## Fixed effects:  list(fixed) 
##                       Value  Std.Error    DF  t-value p-value
## X(Intercept)     0.09897610 0.05442830 25564 1.818468  0.0690
## Xs(Longevity)Fx1 0.07136657 0.06098446    46 1.170242  0.2479
##  Correlation: 
##                  X(Int)
## Xs(Longevity)Fx1 -0.21 
## 
## Standardized Within-Group Residuals:
##        Min         Q1        Med         Q3        Max 
## -1.7054544 -0.4904136 -0.1750471  0.1644542 18.5844194 
## 
## Number of Observations: 25612
## Number of Groups: 
##           g File %in% g 
##           1          48
summary(gammT_longevity$lme)
## Linear mixed-effects model fit by maximum likelihood
##   Data: data 
##        AIC      BIC    logLik
##   16383.01 16423.76 -8186.505
## 
## Random effects:
##  Formula: ~Xr - 1 | g
##  Structure: pdIdnot
##              Xr1      Xr2      Xr3      Xr4      Xr5      Xr6      Xr7      Xr8
## StdDev: 1.747362 1.747362 1.747362 1.747362 1.747362 1.747362 1.747362 1.747362
## 
##  Formula: ~1 | Transmitter %in% g
##         (Intercept) Residual
## StdDev:   0.3568017 0.316441
## 
## Variance function:
##  Structure: fixed weights
##  Formula: ~invwt 
## Fixed effects:  list(fixed) 
##                       Value  Std.Error    DF   t-value p-value
## X(Intercept)      0.0976919 0.01432626 24761  6.819078  0.0000
## Xs(Longevity)Fx1 -0.1317471 0.23120798 24761 -0.569821  0.5688
##  Correlation: 
##                  X(Int)
## Xs(Longevity)Fx1 0.032 
## 
## Standardized Within-Group Residuals:
##         Min          Q1         Med          Q3         Max 
## -2.37102553 -0.43691326 -0.10609358  0.09348865 16.73749753 
## 
## Number of Observations: 25612
## Number of Groups: 
##                  g Transmitter %in% g 
##                  1                850
summary(gammS_longevity$lme)
## Linear mixed-effects model fit by maximum likelihood
##   Data: data 
##        AIC      BIC    logLik
##   38999.43 39040.18 -19494.71
## 
## Random effects:
##  Formula: ~Xr - 1 | g
##  Structure: pdIdnot
##                  Xr1          Xr2          Xr3          Xr4          Xr5
## StdDev: 0.0004036288 0.0004036288 0.0004036288 0.0004036288 0.0004036288
##                  Xr6          Xr7          Xr8
## StdDev: 0.0004036288 0.0004036288 0.0004036288
## 
##  Formula: ~1 | Species %in% g
##         (Intercept)  Residual
## StdDev:   0.3768419 0.5165025
## 
## Variance function:
##  Structure: fixed weights
##  Formula: ~invwt 
## Fixed effects:  list(fixed) 
##                       Value  Std.Error    DF  t-value p-value
## X(Intercept)     0.13080732 0.07596754 25582 1.721884  0.0851
## Xs(Longevity)Fx1 0.08237376 0.09897957    28 0.832230  0.4123
##  Correlation: 
##                  X(Int)
## Xs(Longevity)Fx1 -0.383
## 
## Standardized Within-Group Residuals:
##        Min         Q1        Med         Q3        Max 
## -1.4456090 -0.4384991 -0.1606068  0.1335295 16.7637689 
## 
## Number of Observations: 25612
## Number of Groups: 
##              g Species %in% g 
##              1             30
#Vulnerability
lm_vulnerability <- lm(KUD95 ~ Vulnerability, data=week_kuds)
glm_vulnerability <- glm(KUD95 ~ Vulnerability, data=week_kuds, family=Gamma(link="log"))
gam_vulnerability <- gam(KUD95 ~ Vulnerability, data=week_kuds, family=Gamma(link="log"))
glmmF_vulnerability <- glmmTMB(KUD95 ~ Vulnerability + (1|File), data=week_kuds, family=Gamma(link="log"))
glmmT_vulnerability <- glmmTMB(KUD95 ~ Vulnerability + (1|Transmitter), data=week_kuds, family=Gamma(link="log"))
glmmS_vulnerability <- glmmTMB(KUD95 ~ Vulnerability + (1|Species), data=week_kuds, family=Gamma(link="log"))
gammF_vulnerability <- gamm(KUD95 ~ s(Vulnerability), random=list(File=~1), data= week_kuds, family=Gamma(link="log"))   #Preference for gmcv package since gamm4 uses glm methods
## 
##  Maximum number of PQL iterations:  20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
#gamm4F_vulnerability <- gamm4(KUD95 ~ s(Vulnerability), random = ~(1|File), data = week_kuds, family=Gamma(link="log"))
gammT_vulnerability <- gamm(KUD95 ~ s(Vulnerability), random=list(Transmitter=~1), data= week_kuds, family=Gamma(link="log"))   #Preference for gmcv package since gamm4 uses glm methods
## 
##  Maximum number of PQL iterations:  20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
## iteration 5
## iteration 6
## iteration 7
#gamm4T_vulnerability <- gamm4(KUD95 ~ s(Vulnerability), random = ~(1|Transmitter), data = week_kuds, family=Gamma(link="log"))
gammS_vulnerability <- gamm(KUD95 ~ s(Vulnerability), random=list(Species=~1), data= week_kuds, family=Gamma(link="log"))   #Preference for gmcv package since gamm4 uses glm methods
## 
##  Maximum number of PQL iterations:  20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
#gamm4S_vulnerability <- gamm4(KUD95 ~ s(Vulnerability), random = ~(1|Species), data = week_kuds, family=Gamma(link="log"))

AIC(lm_vulnerability, glm_vulnerability, gam_vulnerability, glmmF_vulnerability, glmmT_vulnerability, glmmS_vulnerability)
##                     df      AIC
## lm_vulnerability     3 60357.98
## glm_vulnerability    3 35242.26
## gam_vulnerability    3 42706.31
## glmmF_vulnerability  4 20491.43
## glmmT_vulnerability  4 10332.37
## glmmS_vulnerability  4 25624.76
summary(gammF_vulnerability$lme)
## Linear mixed-effects model fit by maximum likelihood
##   Data: data 
##        AIC      BIC    logLik
##   30654.78 30695.53 -15322.39
## 
## Random effects:
##  Formula: ~Xr - 1 | g
##  Structure: pdIdnot
##                Xr1        Xr2        Xr3        Xr4        Xr5        Xr6
## StdDev: 0.00204696 0.00204696 0.00204696 0.00204696 0.00204696 0.00204696
##                Xr7        Xr8
## StdDev: 0.00204696 0.00204696
## 
##  Formula: ~1 | File %in% g
##         (Intercept)  Residual
## StdDev:   0.3631843 0.4380816
## 
## Variance function:
##  Structure: fixed weights
##  Formula: ~invwt 
## Fixed effects:  list(fixed) 
##                           Value  Std.Error    DF  t-value p-value
## X(Intercept)         0.13813530 0.05767679 25564 2.394989  0.0166
## Xs(Vulnerability)Fx1 0.05038345 0.04332497    46 1.162919  0.2509
##  Correlation: 
##                      X(Int)
## Xs(Vulnerability)Fx1 0.384 
## 
## Standardized Within-Group Residuals:
##        Min         Q1        Med         Q3        Max 
## -1.7052382 -0.4904786 -0.1745979  0.1643848 18.5889624 
## 
## Number of Observations: 25612
## Number of Groups: 
##           g File %in% g 
##           1          48
summary(gammT_vulnerability$lme)
## Linear mixed-effects model fit by maximum likelihood
##   Data: data 
##        AIC      BIC    logLik
##   16440.07 16480.82 -8215.033
## 
## Random effects:
##  Formula: ~Xr - 1 | g
##  Structure: pdIdnot
##                  Xr1          Xr2          Xr3          Xr4          Xr5
## StdDev: 9.412236e-05 9.412236e-05 9.412236e-05 9.412236e-05 9.412236e-05
##                  Xr6          Xr7          Xr8
## StdDev: 9.412236e-05 9.412236e-05 9.412236e-05
## 
##  Formula: ~1 | Transmitter %in% g
##         (Intercept)  Residual
## StdDev:   0.3764228 0.3164636
## 
## Variance function:
##  Structure: fixed weights
##  Formula: ~invwt 
## Fixed effects:  list(fixed) 
##                            Value  Std.Error    DF   t-value p-value
## X(Intercept)          0.09368621 0.01367981 24761  6.848502  0.0000
## Xs(Vulnerability)Fx1 -0.01404752 0.01209911 24761 -1.161037  0.2456
##  Correlation: 
##                      X(Int)
## Xs(Vulnerability)Fx1 0.174 
## 
## Standardized Within-Group Residuals:
##         Min          Q1         Med          Q3         Max 
## -2.37336873 -0.43401081 -0.10727478  0.09191304 16.68618796 
## 
## Number of Observations: 25612
## Number of Groups: 
##                  g Transmitter %in% g 
##                  1                850
summary(gammS_vulnerability$lme)
## Linear mixed-effects model fit by maximum likelihood
##   Data: data 
##        AIC      BIC    logLik
##   38995.83 39036.58 -19492.91
## 
## Random effects:
##  Formula: ~Xr - 1 | g
##  Structure: pdIdnot
##                 Xr1         Xr2         Xr3         Xr4         Xr5         Xr6
## StdDev: 0.008092824 0.008092824 0.008092824 0.008092824 0.008092824 0.008092824
##                 Xr7         Xr8
## StdDev: 0.008092824 0.008092824
## 
##  Formula: ~1 | Species %in% g
##         (Intercept)  Residual
## StdDev:   0.3686247 0.5164789
## 
## Variance function:
##  Structure: fixed weights
##  Formula: ~invwt 
## Fixed effects:  list(fixed) 
##                           Value  Std.Error    DF  t-value p-value
## X(Intercept)         0.21470748 0.07948504 25582 2.701231  0.0069
## Xs(Vulnerability)Fx1 0.07523367 0.05028284    28 1.496210  0.1458
##  Correlation: 
##                      X(Int)
## Xs(Vulnerability)Fx1 0.5   
## 
## Standardized Within-Group Residuals:
##        Min         Q1        Med         Q3        Max 
## -1.4452047 -0.4386084 -0.1609825  0.1333185 16.7607449 
## 
## Number of Observations: 25612
## Number of Groups: 
##              g Species %in% g 
##              1             30
#Troph
lm_troph <- lm(KUD95 ~ Troph, data=week_kuds)
glm_troph <- glm(KUD95 ~ Troph, data=week_kuds, family=Gamma(link="log"))
gam_troph <- gam(KUD95 ~ Troph, data=week_kuds, family=Gamma(link="log"))
glmmF_troph <- glmmTMB(KUD95 ~ Troph + (1|File), data=week_kuds, family=Gamma(link="log"))
glmmT_troph <- glmmTMB(KUD95 ~ Troph + (1|Transmitter), data=week_kuds, family=Gamma(link="log"))
glmmS_troph <- glmmTMB(KUD95 ~ Troph + (1|Species), data=week_kuds, family=Gamma(link="log"))
gammF_troph <- gamm(KUD95 ~ s(Troph), random=list(File=~1), data= week_kuds, family=Gamma(link="log"))   ##Preference for gmcv package since gamm4 uses glm methods
## 
##  Maximum number of PQL iterations:  20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
#gamm4F_troph <- gamm4(KUD95 ~ s(Troph), random = ~(1|File), data = week_kuds, family=Gamma(link="log"))
gammT_troph <- gamm(KUD95 ~ s(Troph), random=list(Transmitter=~1), data= week_kuds, family=Gamma(link="log"))   #Preference for gmcv package since gamm4 uses glm methods
## 
##  Maximum number of PQL iterations:  20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
## iteration 5
## iteration 6
## iteration 7
#gamm4T_troph <- gamm4(KUD95 ~ s(Troph), random = ~(1|Transmitter), data = week_kuds, family=Gamma(link="log"))
gammS_troph <- gamm(KUD95 ~ s(Troph), random=list(Species=~1), data= week_kuds, family=Gamma(link="log"))   #Preference for gmcv package since gamm4 uses glm methods
## 
##  Maximum number of PQL iterations:  20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
#gamm4S_troph <- gamm4(KUD95 ~ s(Troph), random = ~(1|Species), data = week_kuds, family=Gamma(link="log"))

AIC(lm_troph, glm_troph, gam_troph, glmmF_troph, glmmT_troph, glmmS_troph)
##             df      AIC
## lm_troph     3 60325.15
## glm_troph    3 35173.93
## gam_troph    3 42397.54
## glmmF_troph  4 20488.42
## glmmT_troph  4 10328.52
## glmmS_troph  4 25622.11
summary(gammF_troph$lme)
## Linear mixed-effects model fit by maximum likelihood
##   Data: data 
##        AIC     BIC    logLik
##   30654.85 30695.6 -15322.42
## 
## Random effects:
##  Formula: ~Xr - 1 | g
##  Structure: pdIdnot
##                 Xr1         Xr2         Xr3         Xr4         Xr5         Xr6
## StdDev: 0.003022293 0.003022293 0.003022293 0.003022293 0.003022293 0.003022293
##                 Xr7         Xr8
## StdDev: 0.003022293 0.003022293
## 
##  Formula: ~1 | File %in% g
##         (Intercept)  Residual
## StdDev:   0.3514998 0.4381082
## 
## Variance function:
##  Structure: fixed weights
##  Formula: ~invwt 
## Fixed effects:  list(fixed) 
##                  Value  Std.Error    DF  t-value p-value
## X(Intercept) 0.1407928 0.05328207 25564 2.642404  0.0082
## Xs(Troph)Fx1 0.1094933 0.05128460    46 2.135013  0.0381
##  Correlation: 
##              X(Int)
## Xs(Troph)Fx1 0.25  
## 
## Standardized Within-Group Residuals:
##        Min         Q1        Med         Q3        Max 
## -1.7057735 -0.4905407 -0.1743593  0.1641210 18.5869203 
## 
## Number of Observations: 25612
## Number of Groups: 
##           g File %in% g 
##           1          48
summary(gammT_troph$lme)
## Linear mixed-effects model fit by maximum likelihood
##   Data: data 
##        AIC      BIC    logLik
##   16315.53 16356.28 -8152.763
## 
## Random effects:
##  Formula: ~Xr - 1 | g
##  Structure: pdIdnot
##              Xr1      Xr2      Xr3      Xr4      Xr5      Xr6      Xr7      Xr8
## StdDev: 1.515327 1.515327 1.515327 1.515327 1.515327 1.515327 1.515327 1.515327
## 
##  Formula: ~1 | Transmitter %in% g
##         (Intercept)  Residual
## StdDev:   0.3407553 0.3164768
## 
## Variance function:
##  Structure: fixed weights
##  Formula: ~invwt 
## Fixed effects:  list(fixed) 
##                  Value  Std.Error    DF  t-value p-value
## X(Intercept) 0.1015642 0.01384182 24761 7.337492   0e+00
## Xs(Troph)Fx1 0.9982972 0.28366773 24761 3.519248   4e-04
##  Correlation: 
##              X(Int)
## Xs(Troph)Fx1 -0.171
## 
## Standardized Within-Group Residuals:
##         Min          Q1         Med          Q3         Max 
## -2.37628411 -0.43727484 -0.10766155  0.09489305 16.50747430 
## 
## Number of Observations: 25612
## Number of Groups: 
##                  g Transmitter %in% g 
##                  1                850
summary(gammS_troph$lme)
## Linear mixed-effects model fit by maximum likelihood
##   Data: data 
##        AIC      BIC    logLik
##   38996.26 39037.01 -19493.13
## 
## Random effects:
##  Formula: ~Xr - 1 | g
##  Structure: pdIdnot
##                Xr1        Xr2        Xr3        Xr4        Xr5        Xr6
## StdDev: 0.01037979 0.01037979 0.01037979 0.01037979 0.01037979 0.01037979
##                Xr7        Xr8
## StdDev: 0.01037979 0.01037979
## 
##  Formula: ~1 | Species %in% g
##         (Intercept)  Residual
## StdDev:   0.3513253 0.5165112
## 
## Variance function:
##  Structure: fixed weights
##  Formula: ~invwt 
## Fixed effects:  list(fixed) 
##                  Value  Std.Error    DF  t-value p-value
## X(Intercept) 0.1911215 0.06745823 25582 2.833183  0.0046
## Xs(Troph)Fx1 0.1380874 0.06046407    28 2.283793  0.0302
##  Correlation: 
##              X(Int)
## Xs(Troph)Fx1 0.233 
## 
## Standardized Within-Group Residuals:
##        Min         Q1        Med         Q3        Max 
## -1.4459125 -0.4386181 -0.1609980  0.1332556 16.7594377 
## 
## Number of Observations: 25612
## Number of Groups: 
##              g Species %in% g 
##              1             30
#Habitat
lm_habitat <- lm(KUD95 ~ Habitat, data=week_kuds)
glm_habitat <- glm(KUD95 ~ Habitat, data=week_kuds, family=Gamma(link="log"))
gam_habitat <- gam(KUD95 ~ Habitat, data=week_kuds, family=Gamma(link="log"))
glmmF_habitat <- glmmTMB(KUD95 ~ Habitat + (1|File), data=week_kuds, family=Gamma(link="log"))
glmmT_habitat <- glmmTMB(KUD95 ~ Habitat + (1|Transmitter), data=week_kuds, family=Gamma(link="log"))
glmmS_habitat <- glmmTMB(KUD95 ~ Habitat + (1|Species), data=week_kuds, family=Gamma(link="log"))
gammF_habitat <- gamm(KUD95 ~ Habitat, random=list(File=~1), data= week_kuds, family=Gamma(link="log"))   #Preference for gmcv package since gamm4 uses glm methods
## 
##  Maximum number of PQL iterations:  20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
#gamm4F_habitat <- gamm4(KUD95 ~ Habitat, random = ~(1|File), data = week_kuds, family=Gamma(link="log"))
gammT_habitat <- gamm(KUD95 ~ Habitat, random=list(Transmitter=~1), data= week_kuds, family=Gamma(link="log"))   #Preference for gmcv package since gamm4 uses glm methods
## 
##  Maximum number of PQL iterations:  20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
## iteration 5
## iteration 6
## iteration 7
## iteration 8
#gamm4T_habitat <- gamm4(KUD95 ~ Habitat, random = ~(1|Transmitter), data = week_kuds, family=Gamma(link="log"))
gammS_habitat <- gamm(KUD95 ~ Habitat, random=list(Species=~1), data= week_kuds, family=Gamma(link="log"))   #Preference for gmcv package since gamm4 uses glm methods
## 
##  Maximum number of PQL iterations:  20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
#gamm4S_habitat <- gamm4(KUD95 ~ Habitat, random = ~(1|Species), data = week_kuds, family=Gamma(link="log"))

AIC(lm_habitat, glm_habitat, gam_habitat, glmmF_habitat, glmmT_habitat, glmmS_habitat)
##               df      AIC
## lm_habitat     4 59545.84
## glm_habitat    4 33309.53
## gam_habitat    4 39499.58
## glmmF_habitat  5 20467.43
## glmmT_habitat  5 10242.59
## glmmS_habitat  5 25608.63
summary(gammF_habitat$lme)
## Linear mixed-effects model fit by maximum likelihood
##   Data: data 
##        AIC      BIC    logLik
##   30636.69 30677.44 -15313.34
## 
## Random effects:
##  Formula: ~1 | File
##         (Intercept)  Residual
## StdDev:   0.2754513 0.4381452
## 
## Variance function:
##  Structure: fixed weights
##  Formula: ~invwt 
## Fixed effects:  list(fixed) 
##                              Value  Std.Error    DF   t-value p-value
## X(Intercept)             0.1760671 0.07221588 25564  2.438067  0.0148
## XHabitatdemersal        -0.2361342 0.09064370    45 -2.605082  0.0124
## XHabitatpelagic-neritic  0.5137363 0.13455430    45  3.818059  0.0004
##  Correlation: 
##                         X(Int) XHbttd
## XHabitatdemersal        -0.797       
## XHabitatpelagic-neritic -0.537  0.428
## 
## Standardized Within-Group Residuals:
##        Min         Q1        Med         Q3        Max 
## -1.7063792 -0.4913755 -0.1750669  0.1649180 18.5981998 
## 
## Number of Observations: 25612
## Number of Groups: 48
summary(gammT_habitat$lme)
## Linear mixed-effects model fit by maximum likelihood
##   Data: data 
##        AIC      BIC    logLik
##   16323.07 16363.82 -8156.535
## 
## Random effects:
##  Formula: ~1 | Transmitter
##         (Intercept)  Residual
## StdDev:   0.3549725 0.3162995
## 
## Variance function:
##  Structure: fixed weights
##  Formula: ~invwt 
## Fixed effects:  list(fixed) 
##                              Value  Std.Error    DF   t-value p-value
## X(Intercept)             0.1343299 0.01855173 24761  7.240826       0
## XHabitatdemersal        -0.1404723 0.02617063 24761 -5.367553       0
## XHabitatpelagic-neritic  0.3266083 0.04914857   848  6.645327       0
##  Correlation: 
##                         X(Int) XHbttd
## XHabitatdemersal        -0.698       
## XHabitatpelagic-neritic -0.377  0.263
## 
## Standardized Within-Group Residuals:
##         Min          Q1         Med          Q3         Max 
## -2.37874372 -0.43561796 -0.10557924  0.09505689 16.50372555 
## 
## Number of Observations: 25612
## Number of Groups: 850
summary(gammS_habitat$lme)
## Linear mixed-effects model fit by maximum likelihood
##   Data: data 
##        AIC      BIC    logLik
##   38977.48 39018.24 -19483.74
## 
## Random effects:
##  Formula: ~1 | Species
##         (Intercept)  Residual
## StdDev:   0.2703393 0.5164722
## 
## Variance function:
##  Structure: fixed weights
##  Formula: ~invwt 
## Fixed effects:  list(fixed) 
##                              Value  Std.Error    DF   t-value p-value
## X(Intercept)             0.1549188 0.09159865 25582  1.691278  0.0908
## XHabitatdemersal        -0.1894074 0.11578948    27 -1.635791  0.1135
## XHabitatpelagic-neritic  0.5659141 0.15318118    27  3.694410  0.0010
##  Correlation: 
##                         X(Int) XHbttd
## XHabitatdemersal        -0.791       
## XHabitatpelagic-neritic -0.598  0.473
## 
## Standardized Within-Group Residuals:
##        Min         Q1        Med         Q3        Max 
## -1.4465279 -0.4386535 -0.1607899  0.1336594 16.7629675 
## 
## Number of Observations: 25612
## Number of Groups: 30
#Migration
lm_migration <- lm(KUD95 ~ Migration, data=week_kuds)
glm_migration <- glm(KUD95 ~ Migration, data=week_kuds, family=Gamma(link="log"))
gam_migration <- gam(KUD95 ~ Migration, data=week_kuds, family=Gamma(link="log"))
glmmF_migration <- glmmTMB(KUD95 ~ Migration + (1|File), data=week_kuds, family=Gamma(link="log"))
glmmT_migration <- glmmTMB(KUD95 ~ Migration + (1|Transmitter), data=week_kuds, family=Gamma(link="log"))
glmmS_migration <- glmmTMB(KUD95 ~ Migration + (1|Species), data=week_kuds, family=Gamma(link="log"))
gammF_migration <- gamm(KUD95 ~ Migration, random=list(File=~1), data= week_kuds, family=Gamma(link="log"))   #Preference for gmcv package since gamm4 uses glm methods
## 
##  Maximum number of PQL iterations:  20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
#gamm4F_migration <- gamm4(KUD95 ~ Migration, random = ~(1|File), data = week_kuds, family=Gamma(link="log"))
gammT_migration <- gamm(KUD95 ~ Migration, random=list(Transmitter=~1), data= week_kuds, family=Gamma(link="log"))   #Preference for gmcv package since gamm4 uses glm methods
## 
##  Maximum number of PQL iterations:  20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
## iteration 5
## iteration 6
## iteration 7
## iteration 8
#gamm4T_migration <- gamm4(KUD95 ~ Migration, random = ~(1|Transmitter), data = week_kuds, family=Gamma(link="log"))
gammS_migration <- gamm(KUD95 ~ Migration, random=list(Species=~1), data= week_kuds, family=Gamma(link="log"))   #Preference for gmcv package since gamm4 uses glm methods
## 
##  Maximum number of PQL iterations:  20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
#gamm4S_migration <- gamm4(KUD95 ~ Migration, random = ~(1|Species), data = week_kuds, family=Gamma(link="log"))

AIC(lm_migration, glm_migration, gam_migration, glmmF_migration, glmmT_migration, glmmS_migration)
##                 df      AIC
## lm_migration     3 59783.05
## glm_migration    3 33916.45
## gam_migration    3 40261.07
## glmmF_migration  4 20480.58
## glmmT_migration  4 10304.26
## glmmS_migration  4 25616.83
summary(gammF_migration$lme)
## Linear mixed-effects model fit by maximum likelihood
##   Data: data 
##        AIC      BIC    logLik
##   30647.16 30679.77 -15319.58
## 
## Random effects:
##  Formula: ~1 | File
##         (Intercept) Residual
## StdDev:   0.3232532 0.438126
## 
## Variance function:
##  Structure: fixed weights
##  Formula: ~invwt 
## Fixed effects:  list(fixed) 
##                              Value Std.Error    DF   t-value p-value
## X(Intercept)            -0.0033103 0.0567591 25564 -0.058322  0.9535
## XMigrationoceanodromous  0.3872820 0.1039663    46  3.725073  0.0005
##  Correlation: 
##                         X(Int)
## XMigrationoceanodromous -0.546
## 
## Standardized Within-Group Residuals:
##        Min         Q1        Med         Q3        Max 
## -1.7055110 -0.4907120 -0.1752456  0.1643914 18.5908519 
## 
## Number of Observations: 25612
## Number of Groups: 48
summary(gammT_migration$lme)
## Linear mixed-effects model fit by maximum likelihood
##   Data: data 
##        AIC      BIC    logLik
##   16400.94 16433.55 -8196.472
## 
## Random effects:
##  Formula: ~1 | Transmitter
##         (Intercept)  Residual
## StdDev:   0.3684918 0.3164387
## 
## Variance function:
##  Structure: fixed weights
##  Formula: ~invwt 
## Fixed effects:  list(fixed) 
##                              Value  Std.Error    DF  t-value p-value
## X(Intercept)            0.03095838 0.01767476 24762 1.751558  0.0799
## XMigrationoceanodromous 0.14805088 0.02659811   848 5.566217  0.0000
##  Correlation: 
##                         X(Int)
## XMigrationoceanodromous -0.665
## 
## Standardized Within-Group Residuals:
##         Min          Q1         Med          Q3         Max 
## -2.37391839 -0.43407999 -0.10603433  0.09325078 16.65302290 
## 
## Number of Observations: 25612
## Number of Groups: 850
summary(gammS_migration$lme)
## Linear mixed-effects model fit by maximum likelihood
##   Data: data 
##        AIC      BIC    logLik
##   38982.97 39015.58 -19487.49
## 
## Random effects:
##  Formula: ~1 | Species
##         (Intercept)  Residual
## StdDev:    0.320519 0.5164504
## 
## Variance function:
##  Structure: fixed weights
##  Formula: ~invwt 
## Fixed effects:  list(fixed) 
##                             Value  Std.Error    DF  t-value p-value
## X(Intercept)            0.0149861 0.07229769 25582 0.207284  0.8358
## XMigrationoceanodromous 0.4497332 0.12966586    28 3.468401  0.0017
##  Correlation: 
##                         X(Int)
## XMigrationoceanodromous -0.558
## 
## Standardized Within-Group Residuals:
##        Min         Q1        Med         Q3        Max 
## -1.4458140 -0.4389911 -0.1607780  0.1337875 16.7638686 
## 
## Number of Observations: 25612
## Number of Groups: 30
#ComImport
lm_comimport <- lm(KUD95 ~ ComImport, data=week_kuds)
glm_comimport <- glm(KUD95 ~ ComImport, data=week_kuds, family=Gamma(link="log"))
gam_comimport <- gam(KUD95 ~ ComImport, data=week_kuds, family=Gamma(link="log"))
glmmF_comimport <- glmmTMB(KUD95 ~ ComImport + (1|File), data=week_kuds, family=Gamma(link="log"))
glmmT_comimport <- glmmTMB(KUD95 ~ ComImport + (1|Transmitter), data=week_kuds, family=Gamma(link="log"))
glmmS_comimport <- glmmTMB(KUD95 ~ ComImport + (1|Species), data=week_kuds, family=Gamma(link="log"))
gammF_comimport <- gamm(KUD95 ~ ComImport, random=list(File=~1), data= week_kuds, family=Gamma(link="log"))   #Preference for gmcv package since gamm4 uses glm methods
## 
##  Maximum number of PQL iterations:  20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
#gamm4F_comimport <- gamm4(KUD95 ~ ComImport, random = ~(1|File), data = week_kuds, family=Gamma(link="log"))
gammT_comimport <- gamm(KUD95 ~ ComImport, random=list(Transmitter=~1), data= week_kuds, family=Gamma(link="log"))   #Preference for gmcv package since gamm4 uses glm methods
## 
##  Maximum number of PQL iterations:  20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
## iteration 5
## iteration 6
## iteration 7
#gamm4T_comimport <- gamm4(KUD95 ~ ComImport, random = ~(1|Transmitter), data = week_kuds, family=Gamma(link="log"))
gammS_comimport <- gamm(KUD95 ~ ComImport, random=list(Species=~1), data= week_kuds, family=Gamma(link="log"))   #Preference for gmcv package since gamm4 uses glm methods
## 
##  Maximum number of PQL iterations:  20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
#gamm4S_comimport <- gamm4(KUD95 ~ ComImport, random = ~(1|Species), data = week_kuds, family=Gamma(link="log"))

AIC(lm_comimport, glm_comimport, gam_comimport, glmmF_comimport, glmmT_comimport, glmmS_comimport)
##                 df      AIC
## lm_comimport     4 60400.97
## glm_comimport    4 35333.69
## gam_comimport    4 42658.62
## glmmF_comimport  5 20492.95
## glmmT_comimport  5 10318.17
## glmmS_comimport  5 25627.65
summary(gammF_comimport$lme)
## Linear mixed-effects model fit by maximum likelihood
##   Data: data 
##        AIC      BIC    logLik
##   30655.11 30695.86 -15322.55
## 
## Random effects:
##  Formula: ~1 | File
##         (Intercept)  Residual
## StdDev:   0.3613773 0.4380884
## 
## Variance function:
##  Structure: fixed weights
##  Formula: ~invwt 
## Fixed effects:  list(fixed) 
##                        Value  Std.Error    DF    t-value p-value
## X(Intercept)      0.08066130 0.07402136 25564  1.0897031  0.2759
## XComImportmedium  0.12114671 0.11581658    45  1.0460222  0.3011
## XComImportminor  -0.08389121 0.16293504    45 -0.5148752  0.6092
##  Correlation: 
##                  X(Int) XCmImprtmd
## XComImportmedium -0.639           
## XComImportminor  -0.454  0.290    
## 
## Standardized Within-Group Residuals:
##        Min         Q1        Med         Q3        Max 
## -1.7050327 -0.4904828 -0.1744788  0.1642765 18.5872554 
## 
## Number of Observations: 25612
## Number of Groups: 48
summary(gammT_comimport$lme)
## Linear mixed-effects model fit by maximum likelihood
##   Data: data 
##        AIC      BIC    logLik
##   16435.14 16475.89 -8212.569
## 
## Random effects:
##  Formula: ~1 | Transmitter
##         (Intercept)  Residual
## StdDev:   0.3754841 0.3164571
## 
## Variance function:
##  Structure: fixed weights
##  Formula: ~invwt 
## Fixed effects:  list(fixed) 
##                        Value  Std.Error    DF   t-value p-value
## X(Intercept)      0.10330239 0.01543433 24760  6.693025  0.0000
## XComImportmedium -0.00045608 0.03133734 24760 -0.014554  0.9884
## XComImportminor  -0.18577454 0.04724841 24760 -3.931869  0.0001
##  Correlation: 
##                  X(Int) XCmImprtmd
## XComImportmedium -0.479           
## XComImportminor  -0.144  0.069    
## 
## Standardized Within-Group Residuals:
##         Min          Q1         Med          Q3         Max 
## -2.37336109 -0.43452606 -0.10750912  0.09002168 16.68731015 
## 
## Number of Observations: 25612
## Number of Groups: 850
summary(gammS_comimport$lme)
## Linear mixed-effects model fit by maximum likelihood
##   Data: data 
##        AIC      BIC    logLik
##   38995.75 39036.51 -19492.88
## 
## Random effects:
##  Formula: ~1 | Species
##         (Intercept) Residual
## StdDev:    0.372644 0.516472
## 
## Variance function:
##  Structure: fixed weights
##  Formula: ~invwt 
## Fixed effects:  list(fixed) 
##                        Value Std.Error    DF    t-value p-value
## X(Intercept)      0.19053859 0.1180325 25582  1.6142891  0.1065
## XComImportmedium  0.01205211 0.1573842    27  0.0765776  0.9395
## XComImportminor  -0.19545923 0.1922476    27 -1.0167058  0.3183
##  Correlation: 
##                  X(Int) XCmImprtmd
## XComImportmedium -0.750           
## XComImportminor  -0.614  0.460    
## 
## Standardized Within-Group Residuals:
##        Min         Q1        Med         Q3        Max 
## -1.4454682 -0.4385825 -0.1608901  0.1334954 16.7619480 
## 
## Number of Observations: 25612
## Number of Groups: 30

Observing the AIC values of all models adjusted, we can see that the one that better fits the data is the GLMM, with the Transmitter as random effect. Looking for all the GLMM adjusted, with higher attention to the variables used as random effects, we see that the models adjusted with Transmitter are better than the ones adjusted with File, and lastly Species.

To analyse and support the importance of each variable assigned to the random effect, we also perform a random forest model, including all three variables: Transmitter, File and Species.

random_three <- rpart(KUD95 ~ Species + Transmitter + File, data = week_kuds)

summary(random_three)
## Call:
## rpart(formula = KUD95 ~ Species + Transmitter + File, data = week_kuds)
##   n= 25612 
## 
##           CP nsplit rel error    xerror       xstd
## 1 0.33819203      0 1.0000000 1.0000363 0.04300737
## 2 0.04950932      1 0.6618080 0.6878546 0.03090367
## 3 0.04599746      2 0.6122986 0.6348350 0.03026240
## 4 0.01900331      3 0.5663012 0.6036775 0.02872377
## 5 0.01000000      4 0.5472979 0.5919881 0.02779260
## 
## Variable importance
## Transmitter        File     Species 
##          64          24          12 
## 
## Node number 1: 25612 observations,    complexity param=0.338192
##   mean=1.123676, MSE=0.6193676 
##   left son=2 (23133 obs) right son=3 (2479 obs)
##   Primary splits:
##       Transmitter splits as  LLLLLLLLLLLLLLLLLLLLLLLLLLLLLRLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLRLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLRLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLRLLLLLLRLLLLLLRLLLLRLRRLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLRLRLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLRRRRRRRRLRRLRLRLRRLLRRRRLLRLRLLRRRRRRRRLLLRRRRRRRLLRLRLLLLLLRRLLLRLLRLRRRRRRLLLLLRLLLLLLLLRRLLLRLLLLLLLLLLLLRLRLLLLLLRLLLRLRRLLLLLLLLLLLRRLLLLLLLLLLLLLLLLLLRRLLRRRLLLLLLRLRLLLLLLLRLLLLLLLLLLLLRRLLLLLLRLLLLLLLLLLLLLLLLLLLRRLRLLLLRRLLRLRRRLLLRLLLRRLLLRLLLRLLLLLLRLRRRLLLLLRLLRLLLLRRRRLRRLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLRLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLRLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLRRL, improve=0.3381920, (0 missing)
##       File        splits as  LLLLRLLLLLLLLLLLLLLLLLRLLLLRLLLLLLRLLLLLLLLLRLLL, improve=0.2195713, (0 missing)
##       Species     splits as  RRRLRLLLRLLLLLRLRLLLLRLLLLLRLL, improve=0.1174541, (0 missing)
##   Surrogate splits:
##       File    splits as  LLLLRLLLLLLLLLLLLLLLLLRLLLLRLLLLLLRLLLLLLLLLRLLL, agree=0.944, adj=0.416, (0 split)
##       Species splits as  LLLLLLLLRLLLLLRLLLLLLRLLLLLLLL, agree=0.920, adj=0.172, (0 split)
## 
## Node number 2: 23133 observations,    complexity param=0.04950932
##   mean=0.9738526, MSE=0.141553 
##   left son=4 (17858 obs) right son=5 (5275 obs)
##   Primary splits:
##       Transmitter splits as  LLLLLLLLLLLLLLLLLLLLLLLLRLLLR-LLRRRRLLRRLLLLLLRLLRRLLLLRLLLLRRLLRRLLLLRRLRRLRRRRRL-LLLLRRLLLRLLLLRLLRLLLLLLLLRLLLLRLRRLLLLLLRLLLRLLLRLLLRLLLLLLLLLRLRLLRLLLL-RLLLLLLLLRLLLRLRRLRRRLRLRLLLLRLRLRLRLRLLRLLLLLLLLLLLLLLLLLRRLLLLLLLRLLLLLLLLLLLRRLLLLLLLLLLLLLLLLLRLLLLLLRRLR-RLLRLL-LRLLLL-LRLR-R--LLLRLLLLLRLLLLLLLRLLLLLLLLLLLLLLLLLLLLRLLLRLL-R-LLLRLRRRLLRLLLLRRRRLLLRLLLLLLLLLLLLLLLLLLLLLLLLLLLRLLLLLLLLLLLLLLLLLLLLLLLLLLLLLL--------R--R-L-R--RL----LR-L-LL--------RLR-------LL-L-LLLRLL--LLL-RR-L------LRRLR-LRLRLRLR--LRR-RRLLLLLLLLLR-R-LLLLLR-LLL-R--LRLLRRLLLRR--LLLLLLLLLLLLRRLLRR--LL---RLLLLR-R-LLLLRRR-RRLLRRLLRRRR--LRRRRR-RLLLLRRLLLLLLRRRRRR--L-LLRL--LL-R---RLR-LRL--RRR-RLL-RRRLLR-R---RRRLL-LL-LLLL----L--RLRLLLLLRLRLLLRLLRLLRRRLLLRRLRRLRLLRL-RLLRRRRRLRRLRLRLLRLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLRLL-LLRRLLLLLLRRLRLLRRRLRLLRLLLLLLL--L, improve=0.23984350, (0 missing)
##       File        splits as  RRRRRRLLLLLLLRLLLLRLLL-RLLLRRLRLLLRLLLLLLLRLRRLL, improve=0.06895091, (0 missing)
##       Species     splits as  RRRLRLLR-LRRLLRLRLRLLRLLLLLLRL, improve=0.05248863, (0 missing)
##   Surrogate splits:
##       File    splits as  RRRLLRLLLLLLLLLLLLLLLL-RLLLRLLRLLLRLLLLLLLRLRLLL, agree=0.803, adj=0.135, (0 split)
##       Species splits as  RRLLRLLL-LRLLLRLLLLLLRLLLLLLLL, agree=0.796, adj=0.104, (0 split)
## 
## Node number 3: 2479 observations,    complexity param=0.04599746
##   mean=2.521761, MSE=2.914028 
##   left son=6 (1303 obs) right son=7 (1176 obs)
##   Primary splits:
##       Transmitter splits as  -----------------------------L----------------------------------------------------L-------------------------------------------------------------------------L-------------------------------------------------------------------------------------------------------------L------R------R----L-RL---------------------------------------------R-R---------------------------------------------------------------------------------RRRRLLRR-LL-L-R-RL--RRRR--R-R--LRRRRRRL---LRRLRLL--L-R------LR---L--R-LRRLRL-----R--------LL---L------------R-L------R---L-LL-----------LL------------------LL--LLL------L-L-------L------------LL------L-------------------RL-L----LR--L-LRL---L---RL---L---R------R-RRL-----L--R----LRRL-RR-------------------------------------L--------------------------------------------------------------------------L-------------------------------LL-, improve=0.10100800, (0 missing)
##       File        splits as  --LLRLL---LL-L---RLL--R-L-RRL-----R----L--L-R---, improve=0.03657201, (0 missing)
##       Species     splits as  LLRL-LRLR--LLRR-L----R---L-R--, improve=0.03151720, (0 missing)
##   Surrogate splits:
##       File    splits as  --LLRLL---LL-L---LLL--R-L-RRL-----R----L--L-R---, agree=0.743, adj=0.457, (0 split)
##       Species splits as  LLLL-LLLR--LLRR-L----R---L-R--, agree=0.725, adj=0.421, (0 split)
## 
## Node number 4: 17858 observations
##   mean=0.8737102, MSE=0.05085055 
## 
## Node number 5: 5275 observations
##   mean=1.312875, MSE=0.2997303 
## 
## Node number 6: 1303 observations
##   mean=2.006347, MSE=1.278305 
## 
## Node number 7: 1176 observations,    complexity param=0.01900331
##   mean=3.092836, MSE=4.105932 
##   left son=14 (970 obs) right son=15 (206 obs)
##   Primary splits:
##       Transmitter splits as  ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------L------L------L----------------------------------------------L-L---------------------------------------------------------------------------------LRRR--RR------R-L---RLLL--R-L---LLRRLL-----LL-R------R-------R------L--LL-L------L--------------------------L--------L------------------------------------------------------------------------------------------------------R--------L-----L--------R--------L------R-RL---------L-----LR--LL---------------------------------------------------------------------------------------------------------------------------------------------------, improve=0.06243126, (0 missing)
##       Species     splits as  -LR---L-R--L-RR-R----L-----L--, improve=0.02460779, (0 missing)
##       File        splits as  --L-R------------L----R---RRL-----L-------R-L---, improve=0.02460779, (0 missing)
##   Surrogate splits:
##       Species splits as  -LL---L-R--L-LL-R----L-----L--, agree=0.86, adj=0.199, (0 split)
##       File    splits as  --L-L------------L----R---LLL-----L-------R-L---, agree=0.86, adj=0.199, (0 split)
## 
## Node number 14: 970 observations
##   mean=2.859514, MSE=3.533638 
## 
## Node number 15: 206 observations
##   mean=4.191485, MSE=5.337343

As suspected by the AIC of the previous models, the Transmitter has higher importance, followed by the File and then the Species.

To choose which one of these variables include in the model, we tested all possible combinations.

glmmT_total <- glmmTMB(KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + ReceiverDensity + MonitArea_km2 + (1|Transmitter), data=week_kuds, family=Gamma(link="log"))

glmmF_total <- glmmTMB(KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + ReceiverDensity + MonitArea_km2 + (1|File), data=week_kuds, family=Gamma(link="log"))

glmmS_total <- glmmTMB(KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + ReceiverDensity + MonitArea_km2 + (1|Species), data=week_kuds, family=Gamma(link="log"))

glmmTF_total <- glmmTMB(KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + ReceiverDensity + MonitArea_km2 + (1|Transmitter) + (1|File), data=week_kuds, family=Gamma(link="log"))

glmmTS_total <- glmmTMB(KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + ReceiverDensity + MonitArea_km2 + (1|Transmitter) + (1|Species), data=week_kuds, family=Gamma(link="log"))

glmmFS_total <- glmmTMB(KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + ReceiverDensity + MonitArea_km2 + (1|File) + (1|Species), data=week_kuds, family=Gamma(link="log"))

glmmTFS_total <- glmmTMB(KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + ReceiverDensity + MonitArea_km2 + (1|Transmitter) + (1|File) + (1|Species), data=week_kuds, family=Gamma(link="log"))


AIC(glmmT_total, glmmF_total, glmmS_total, glmmTF_total, glmmTS_total, glmmFS_total, glmmTFS_total)
##               df       AIC
## glmmT_total   15  9994.907
## glmmF_total   15 20357.009
## glmmS_total   15 22496.812
## glmmTF_total  16  9849.845
## glmmTS_total  16  9946.192
## glmmFS_total  16 20359.009
## glmmTFS_total 17  9851.845
#Now we need to compare if the models are statistically different or not
anova(glmmT_total, glmmTF_total) #significant differences, this two models differ statistically
## Data: week_kuds
## Models:
## glmmT_total: KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmT_total:     Troph + Habitat + Migration + ComImport + ReceiverDensity + , zi=~0, disp=~1
## glmmT_total:     MonitArea_km2 + (1 | Transmitter), zi=~0, disp=~1
## glmmTF_total: KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmTF_total:     Troph + Habitat + Migration + ComImport + ReceiverDensity + , zi=~0, disp=~1
## glmmTF_total:     MonitArea_km2 + (1 | Transmitter) + (1 | File), zi=~0, disp=~1
##              Df    AIC     BIC  logLik deviance  Chisq Chi Df Pr(>Chisq)    
## glmmT_total  15 9994.9 10117.2 -4982.5   9964.9                             
## glmmTF_total 16 9849.8  9980.3 -4908.9   9817.8 147.06      1  < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(glmmT_total, glmmTFS_total) #significant differences, this two models differ statistically
## Data: week_kuds
## Models:
## glmmT_total: KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmT_total:     Troph + Habitat + Migration + ComImport + ReceiverDensity + , zi=~0, disp=~1
## glmmT_total:     MonitArea_km2 + (1 | Transmitter), zi=~0, disp=~1
## glmmTFS_total: KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmTFS_total:     Troph + Habitat + Migration + ComImport + ReceiverDensity + , zi=~0, disp=~1
## glmmTFS_total:     MonitArea_km2 + (1 | Transmitter) + (1 | File) + (1 | Species), zi=~0, disp=~1
##               Df    AIC     BIC  logLik deviance  Chisq Chi Df Pr(>Chisq)    
## glmmT_total   15 9994.9 10117.2 -4982.5   9964.9                             
## glmmTFS_total 17 9851.8  9990.4 -4908.9   9817.8 147.06      2  < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(glmmTF_total, glmmTFS_total) #non significant differences, this two models do not differ statistically
## Data: week_kuds
## Models:
## glmmTF_total: KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmTF_total:     Troph + Habitat + Migration + ComImport + ReceiverDensity + , zi=~0, disp=~1
## glmmTF_total:     MonitArea_km2 + (1 | Transmitter) + (1 | File), zi=~0, disp=~1
## glmmTFS_total: KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmTFS_total:     Troph + Habitat + Migration + ComImport + ReceiverDensity + , zi=~0, disp=~1
## glmmTFS_total:     MonitArea_km2 + (1 | Transmitter) + (1 | File) + (1 | Species), zi=~0, disp=~1
##               Df    AIC    BIC  logLik deviance Chisq Chi Df Pr(>Chisq)
## glmmTF_total  16 9849.8 9980.3 -4908.9   9817.8                        
## glmmTFS_total 17 9851.8 9990.4 -4908.9   9817.8     0      1          1
anova(glmmTF_total, glmmTS_total) #non significant differences, this two models do not differ statistically
## Data: week_kuds
## Models:
## glmmTF_total: KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmTF_total:     Troph + Habitat + Migration + ComImport + ReceiverDensity + , zi=~0, disp=~1
## glmmTF_total:     MonitArea_km2 + (1 | Transmitter) + (1 | File), zi=~0, disp=~1
## glmmTS_total: KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmTS_total:     Troph + Habitat + Migration + ComImport + ReceiverDensity + , zi=~0, disp=~1
## glmmTS_total:     MonitArea_km2 + (1 | Transmitter) + (1 | Species), zi=~0, disp=~1
##              Df    AIC     BIC  logLik deviance Chisq Chi Df Pr(>Chisq)
## glmmTF_total 16 9849.8  9980.3 -4908.9   9817.8                        
## glmmTS_total 16 9946.2 10076.6 -4957.1   9914.2     0      0          1
anova(glmmTS_total, glmmTFS_total) #significant differences, this two models differ statistically
## Data: week_kuds
## Models:
## glmmTS_total: KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmTS_total:     Troph + Habitat + Migration + ComImport + ReceiverDensity + , zi=~0, disp=~1
## glmmTS_total:     MonitArea_km2 + (1 | Transmitter) + (1 | Species), zi=~0, disp=~1
## glmmTFS_total: KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmTFS_total:     Troph + Habitat + Migration + ComImport + ReceiverDensity + , zi=~0, disp=~1
## glmmTFS_total:     MonitArea_km2 + (1 | Transmitter) + (1 | File) + (1 | Species), zi=~0, disp=~1
##               Df    AIC     BIC  logLik deviance  Chisq Chi Df Pr(>Chisq)    
## glmmTS_total  16 9946.2 10076.6 -4957.1   9914.2                             
## glmmTFS_total 17 9851.8  9990.4 -4908.9   9817.8 96.347      1  < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#choose the simplest model with the lower AIC, that is statistically different from the others, which means the model with Transmitter and File as random effect (glmmTF_total)

Having chosen the best variable to include in the model as random effect, we now must do a backward stepwise selection, to investigate which are the best predictor variables to explain the response behaviour.

First we evaluated the significance of each biological trait in explaining the KUDs alone.

#testing all variables separately using GLMM and File and Transmitter as random effects

summary(glmmTMB(KUD95 ~ LengthStd + MonitArea_km2 + (1|Transmitter) + (1|Species), data=week_kuds, family=Gamma(link="log")))
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ LengthStd + MonitArea_km2 + (1 | Transmitter) + (1 |  
##     Species)
## Data: week_kuds
## 
##      AIC      BIC   logLik deviance df.resid 
##   9957.8  10006.7  -4972.9   9945.8    25606 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.08416  0.2901  
##  Species     (Intercept) 0.06684  0.2585  
## Number of obs: 25612, groups:  Transmitter, 850; Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0753 
## 
## Conditional model:
##                Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   -0.201566   0.077053  -2.616   0.0089 ** 
## LengthStd      0.176107   0.111656   1.577   0.1147    
## MonitArea_km2  0.029072   0.002953   9.845   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(glmmTMB(KUD50 ~ LengthStd + MonitArea_km2 +(1|Transmitter) + (1|Species), data=week_kuds, family=Gamma(link="log")))
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ LengthStd + MonitArea_km2 + (1 | Transmitter) + (1 |  
##     Species)
## Data: week_kuds
## 
##      AIC      BIC   logLik deviance df.resid 
## -76236.8 -76187.9  38124.4 -76248.8    25606 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.07285  0.2699  
##  Species     (Intercept) 0.04900  0.2214  
## Number of obs: 25612, groups:  Transmitter, 850; Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0607 
## 
## Conditional model:
##                Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   -1.749181   0.068961 -25.365   <2e-16 ***
## LengthStd      0.184579   0.102893   1.794   0.0728 .  
## MonitArea_km2  0.022768   0.002736   8.322   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(glmmTMB(KUD95 ~ BodyMassStd + MonitArea_km2 +(1|Transmitter) + (1|Species), data=week_kuds, family=Gamma(link="log")))
##  Family: Gamma  ( log )
## Formula:          
## KUD95 ~ BodyMassStd + MonitArea_km2 + (1 | Transmitter) + (1 |      Species)
## Data: week_kuds
## 
##      AIC      BIC   logLik deviance df.resid 
##   9959.6  10008.5  -4973.8   9947.6    25606 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.08430  0.2903  
##  Species     (Intercept) 0.06393  0.2528  
## Number of obs: 25612, groups:  Transmitter, 850; Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0753 
## 
## Conditional model:
##                Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   -0.135589   0.058957  -2.300   0.0215 *  
## BodyMassStd    0.073449   0.085803   0.856   0.3920    
## MonitArea_km2  0.029775   0.002907  10.242   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(glmmTMB(KUD50 ~ BodyMassStd + MonitArea_km2 +(1|Transmitter) + (1|Species), data=week_kuds, family=Gamma(link="log")))
##  Family: Gamma  ( log )
## Formula:          
## KUD50 ~ BodyMassStd + MonitArea_km2 + (1 | Transmitter) + (1 |      Species)
## Data: week_kuds
## 
##      AIC      BIC   logLik deviance df.resid 
## -76235.1 -76186.2  38123.5 -76247.1    25606 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.07293  0.2700  
##  Species     (Intercept) 0.04742  0.2178  
## Number of obs: 25612, groups:  Transmitter, 850; Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0607 
## 
## Conditional model:
##                Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   -1.684331   0.051960  -32.42   <2e-16 ***
## BodyMassStd    0.096288   0.078459    1.23     0.22    
## MonitArea_km2  0.023477   0.002691    8.72   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(glmmTMB(KUD95 ~ Longevity + MonitArea_km2 +(1|Transmitter) + (1|Species), data=week_kuds, family=Gamma(link="log")))
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Longevity + MonitArea_km2 + (1 | Transmitter) + (1 |  
##     Species)
## Data: week_kuds
## 
##      AIC      BIC   logLik deviance df.resid 
##   9958.4  10007.3  -4973.2   9946.4    25606 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.08440  0.2905  
##  Species     (Intercept) 0.05505  0.2346  
## Number of obs: 25612, groups:  Transmitter, 850; Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0753 
## 
## Conditional model:
##                Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   -0.008399   0.096083  -0.087    0.930    
## Longevity     -0.005493   0.003921  -1.401    0.161    
## MonitArea_km2  0.029971   0.002893  10.359   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(glmmTMB(KUD50 ~ Longevity + MonitArea_km2 +(1|Transmitter) + (1|Species), data=week_kuds, family=Gamma(link="log")))
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Longevity + MonitArea_km2 + (1 | Transmitter) + (1 |  
##     Species)
## Data: week_kuds
## 
##      AIC      BIC   logLik deviance df.resid 
## -76235.5 -76186.5  38123.7 -76247.5    25606 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.07304  0.2703  
##  Species     (Intercept) 0.03915  0.1979  
## Number of obs: 25612, groups:  Transmitter, 850; Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0607 
## 
## Conditional model:
##                Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   -1.568318   0.082303 -19.055   <2e-16 ***
## Longevity     -0.004697   0.003328  -1.411    0.158    
## MonitArea_km2  0.023759   0.002675   8.881   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(glmmTMB(KUD95 ~ Vulnerability +MonitArea_km2 + (1|Transmitter) + (1|Species), data=week_kuds, family=Gamma(link="log")))
##  Family: Gamma  ( log )
## Formula:          
## KUD95 ~ Vulnerability + MonitArea_km2 + (1 | Transmitter) + (1 |      Species)
## Data: week_kuds
## 
##      AIC      BIC   logLik deviance df.resid 
##   9958.4  10007.3  -4973.2   9946.4    25606 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.08425  0.2903  
##  Species     (Intercept) 0.05815  0.2411  
## Number of obs: 25612, groups:  Transmitter, 850; Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0753 
## 
## Conditional model:
##                Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   -0.388654   0.201697  -1.927    0.054 .  
## Vulnerability  0.004764   0.003456   1.378    0.168    
## MonitArea_km2  0.030106   0.002901  10.377   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(glmmTMB(KUD50 ~ Vulnerability + MonitArea_km2 +(1|Transmitter) + (1|Species), data=week_kuds, family=Gamma(link="log")))
##  Family: Gamma  ( log )
## Formula:          
## KUD50 ~ Vulnerability + MonitArea_km2 + (1 | Transmitter) + (1 |      Species)
## Data: week_kuds
## 
##      AIC      BIC   logLik deviance df.resid 
## -76235.4 -76186.5  38123.7 -76247.4    25606 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.07289  0.270   
##  Species     (Intercept) 0.04242  0.206   
## Number of obs: 25612, groups:  Transmitter, 850; Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0607 
## 
## Conditional model:
##                Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   -1.891632   0.175757 -10.763   <2e-16 ***
## Vulnerability  0.004036   0.003010   1.341     0.18    
## MonitArea_km2  0.023845   0.002684   8.885   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(glmmTMB(KUD95 ~ Troph + MonitArea_km2 +(1|Transmitter) + (1|Species), data=week_kuds, family=Gamma(link="log")))
##  Family: Gamma  ( log )
## Formula:          
## KUD95 ~ Troph + MonitArea_km2 + (1 | Transmitter) + (1 | Species)
## Data: week_kuds
## 
##      AIC      BIC   logLik deviance df.resid 
##   9953.8  10002.7  -4970.9   9941.8    25606 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.08423  0.2902  
##  Species     (Intercept) 0.04858  0.2204  
## Number of obs: 25612, groups:  Transmitter, 850; Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0753 
## 
## Conditional model:
##                Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   -1.079861   0.367219  -2.941  0.00328 ** 
## Troph          0.250604   0.095353   2.628  0.00858 ** 
## MonitArea_km2  0.030107   0.002875  10.471  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(glmmTMB(KUD50 ~ Troph +MonitArea_km2 + (1|Transmitter) + (1|Species), data=week_kuds, family=Gamma(link="log")))
##  Family: Gamma  ( log )
## Formula:          
## KUD50 ~ Troph + MonitArea_km2 + (1 | Transmitter) + (1 | Species)
## Data: week_kuds
## 
##      AIC      BIC   logLik deviance df.resid 
## -76239.2 -76190.3  38125.6 -76251.2    25606 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.07289  0.2700  
##  Species     (Intercept) 0.03618  0.1902  
## Number of obs: 25612, groups:  Transmitter, 850; Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0607 
## 
## Conditional model:
##                Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   -2.436515   0.321581  -7.577 3.55e-14 ***
## Troph          0.201743   0.083521   2.415   0.0157 *  
## MonitArea_km2  0.023848   0.002661   8.963  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(glmmTMB(KUD95 ~ Habitat +MonitArea_km2 + (1|Transmitter) + (1|Species), data=week_kuds, family=Gamma(link="log")))
##  Family: Gamma  ( log )
## Formula:          
## KUD95 ~ Habitat + MonitArea_km2 + (1 | Transmitter) + (1 | Species)
## Data: week_kuds
## 
##      AIC      BIC   logLik deviance df.resid 
##   9939.4   9996.4  -4962.7   9925.4    25605 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.08377  0.2894  
##  Species     (Intercept) 0.02695  0.1642  
## Number of obs: 25612, groups:  Transmitter, 850; Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0753 
## 
## Conditional model:
##                        Estimate Std. Error z value Pr(>|z|)    
## (Intercept)            -0.14043    0.06703  -2.095   0.0362 *  
## Habitatdemersal        -0.09329    0.07819  -1.193   0.2328    
## Habitatpelagic-neritic  0.45821    0.10906   4.201 2.65e-05 ***
## MonitArea_km2           0.02946    0.00279  10.558  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(glmmTMB(KUD50 ~ Habitat + MonitArea_km2 +(1|Transmitter) + (1|Species), data=week_kuds, family=Gamma(link="log")))
##  Family: Gamma  ( log )
## Formula:          
## KUD50 ~ Habitat + MonitArea_km2 + (1 | Transmitter) + (1 | Species)
## Data: week_kuds
## 
##      AIC      BIC   logLik deviance df.resid 
## -76250.7 -76193.6  38132.3 -76264.7    25605 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.07246  0.2692  
##  Species     (Intercept) 0.02276  0.1509  
## Number of obs: 25612, groups:  Transmitter, 850; Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0607 
## 
## Conditional model:
##                         Estimate Std. Error z value Pr(>|z|)    
## (Intercept)            -1.669623   0.061760 -27.034  < 2e-16 ***
## Habitatdemersal        -0.089362   0.071965  -1.242 0.214334    
## Habitatpelagic-neritic  0.358056   0.100740   3.554 0.000379 ***
## MonitArea_km2           0.023295   0.002594   8.979  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(glmmTMB(KUD95 ~ Migration +MonitArea_km2 + (1|Transmitter) + (1|Species), data=week_kuds, family=Gamma(link="log")))
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ Migration + MonitArea_km2 + (1 | Transmitter) + (1 |  
##     Species)
## Data: week_kuds
## 
##      AIC      BIC   logLik deviance df.resid 
##   9949.0   9997.9  -4968.5   9937.0    25606 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.08407  0.2899  
##  Species     (Intercept) 0.04060  0.2015  
## Number of obs: 25612, groups:  Transmitter, 850; Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0753 
## 
## Conditional model:
##                         Estimate Std. Error z value Pr(>|z|)    
## (Intercept)            -0.222240   0.054558  -4.073 4.63e-05 ***
## Migrationoceanodromous  0.318065   0.089840   3.540    4e-04 ***
## MonitArea_km2           0.030079   0.002847  10.563  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(glmmTMB(KUD50 ~ Migration +MonitArea_km2 + (1|Transmitter) + (1|Species), data=week_kuds, family=Gamma(link="log")))
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ Migration + MonitArea_km2 + (1 | Transmitter) + (1 |  
##     Species)
## Data: week_kuds
## 
##      AIC      BIC   logLik deviance df.resid 
## -76242.7 -76193.8  38127.4 -76254.7    25606 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.07273  0.2697  
##  Species     (Intercept) 0.03194  0.1787  
## Number of obs: 25612, groups:  Transmitter, 850; Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0607 
## 
## Conditional model:
##                        Estimate Std. Error z value Pr(>|z|)    
## (Intercept)            -1.74380    0.04897  -35.61  < 2e-16 ***
## Migrationoceanodromous  0.25036    0.08053    3.11  0.00188 ** 
## MonitArea_km2           0.02381    0.00264    9.02  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(glmmTMB(KUD95 ~ ComImport + MonitArea_km2 +(1|Transmitter) + (1|Species), data=week_kuds, family=Gamma(link="log")))
##  Family: Gamma  ( log )
## Formula:          KUD95 ~ ComImport + MonitArea_km2 + (1 | Transmitter) + (1 |  
##     Species)
## Data: week_kuds
## 
##      AIC      BIC   logLik deviance df.resid 
##   9960.1  10017.2  -4973.1   9946.1    25605 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.08443  0.2906  
##  Species     (Intercept) 0.05555  0.2357  
## Number of obs: 25612, groups:  Transmitter, 850; Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0753 
## 
## Conditional model:
##                  Estimate Std. Error z value Pr(>|z|)    
## (Intercept)     -0.071169   0.080934  -0.879    0.379    
## ComImportmedium -0.025942   0.104913  -0.247    0.805    
## ComImportminor  -0.191468   0.132724  -1.443    0.149    
## MonitArea_km2    0.029738   0.002889  10.292   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(glmmTMB(KUD50 ~ ComImport + MonitArea_km2 +(1|Transmitter) + (1|Species), data=week_kuds, family=Gamma(link="log")))
##  Family: Gamma  ( log )
## Formula:          KUD50 ~ ComImport + MonitArea_km2 + (1 | Transmitter) + (1 |  
##     Species)
## Data: week_kuds
## 
##      AIC      BIC   logLik deviance df.resid 
## -76235.3 -76178.2  38124.6 -76249.3    25605 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.07311  0.2704  
##  Species     (Intercept) 0.03782  0.1945  
## Number of obs: 25612, groups:  Transmitter, 850; Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0607 
## 
## Conditional model:
##                  Estimate Std. Error z value Pr(>|z|)    
## (Intercept)     -1.611860   0.068024 -23.696   <2e-16 ***
## ComImportmedium -0.026675   0.087647  -0.304   0.7609    
## ComImportminor  -0.211400   0.111867  -1.890   0.0588 .  
## MonitArea_km2    0.023475   0.002662   8.820   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

We found that length, trophic level, habitat and migration could alone explain the variability of KUDs. However, we investigated if the models could reach a better fit by including more variables. We did that be performing Backward Elimination.

Final1 <- glmmTMB(KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + ReceiverDensity + MonitArea_km2 + (1|Transmitter) + (1|Species), data = week_kuds, family = Gamma(link="log"))
summary(Final1)
##  Family: Gamma  ( log )
## Formula:          
## KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability +  
##     Troph + Habitat + Migration + ComImport + ReceiverDensity +  
##     MonitArea_km2 + (1 | Transmitter) + (1 | Species)
## Data: week_kuds
## 
##      AIC      BIC   logLik deviance df.resid 
##   9946.2  10076.6  -4957.1   9914.2    25596 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.08369  0.2893  
##  Species     (Intercept) 0.01928  0.1389  
## Number of obs: 25612, groups:  Transmitter, 850; Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0753 
## 
## Conditional model:
##                          Estimate Std. Error z value Pr(>|z|)    
## (Intercept)            -0.1752696  0.3500228  -0.501   0.6166    
## LengthStd               0.2872585  0.1560424   1.841   0.0656 .  
## BodyMassStd            -0.0693491  0.1132226  -0.613   0.5402    
## Longevity              -0.0020923  0.0033787  -0.619   0.5357    
## Vulnerability          -0.0050757  0.0041502  -1.223   0.2213    
## Troph                   0.0774665  0.1068172   0.725   0.4683    
## Habitatdemersal        -0.0831832  0.0980336  -0.849   0.3961    
## Habitatpelagic-neritic  0.3914135  0.1531719   2.555   0.0106 *  
## Migrationoceanodromous  0.1246225  0.1224664   1.018   0.3089    
## ComImportmedium        -0.1587844  0.0704845  -2.253   0.0243 *  
## ComImportminor         -0.1796319  0.1124642  -1.597   0.1102    
## ReceiverDensity         0.0003696  0.0006197   0.596   0.5509    
## MonitArea_km2           0.0292048  0.0036728   7.952 1.84e-15 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Final2 <- glmmTMB(KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + MonitArea_km2 + (1|Transmitter) + (1|Species), data = week_kuds, family = Gamma(link="log"))
summary(Final2)
##  Family: Gamma  ( log )
## Formula:          
## KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability +  
##     Troph + Habitat + Migration + ComImport + MonitArea_km2 +  
##     (1 | Transmitter) + (1 | Species)
## Data: week_kuds
## 
##      AIC      BIC   logLik deviance df.resid 
##   9944.5  10066.8  -4957.3   9914.5    25597 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.08382  0.2895  
##  Species     (Intercept) 0.01900  0.1378  
## Number of obs: 25612, groups:  Transmitter, 850; Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0753 
## 
## Conditional model:
##                         Estimate Std. Error z value Pr(>|z|)    
## (Intercept)            -0.181076   0.348111  -0.520   0.6029    
## LengthStd               0.291368   0.155748   1.871   0.0614 .  
## BodyMassStd            -0.075598   0.112526  -0.672   0.5017    
## Longevity              -0.002075   0.003358  -0.618   0.5366    
## Vulnerability          -0.005287   0.004115  -1.285   0.1988    
## Troph                   0.085869   0.105323   0.815   0.4149    
## Habitatdemersal        -0.083594   0.097555  -0.857   0.3915    
## Habitatpelagic-neritic  0.376237   0.150171   2.505   0.0122 *  
## Migrationoceanodromous  0.131005   0.121277   1.080   0.2800    
## ComImportmedium        -0.155050   0.069822  -2.221   0.0264 *  
## ComImportminor         -0.176964   0.111865  -1.582   0.1137    
## MonitArea_km2           0.027850   0.002885   9.652   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Final3 <- glmmTMB(KUD95 ~ LengthStd + BodyMassStd + Vulnerability + Troph + Habitat + Migration + ComImport + MonitArea_km2 + (1|Transmitter) + (1|Species), data = week_kuds, family = Gamma(link="log"))
summary(Final3)
##  Family: Gamma  ( log )
## Formula:          
## KUD95 ~ LengthStd + BodyMassStd + Vulnerability + Troph + Habitat +  
##     Migration + ComImport + MonitArea_km2 + (1 | Transmitter) +  
##     (1 | Species)
## Data: week_kuds
## 
##      AIC      BIC   logLik deviance df.resid 
##   9942.9  10057.0  -4957.5   9914.9    25598 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.08382  0.2895  
##  Species     (Intercept) 0.01940  0.1393  
## Number of obs: 25612, groups:  Transmitter, 850; Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0753 
## 
## Conditional model:
##                         Estimate Std. Error z value Pr(>|z|)    
## (Intercept)            -0.133518   0.342009  -0.390  0.69625    
## LengthStd               0.296191   0.155860   1.900  0.05739 .  
## BodyMassStd            -0.075325   0.112935  -0.667  0.50479    
## Vulnerability          -0.006247   0.003826  -1.633  0.10250    
## Troph                   0.075257   0.104813   0.718  0.47275    
## Habitatdemersal        -0.085480   0.098149  -0.871  0.38379    
## Habitatpelagic-neritic  0.420951   0.133563   3.152  0.00162 ** 
## Migrationoceanodromous  0.124283   0.121847   1.020  0.30773    
## ComImportmedium        -0.156069   0.070363  -2.218  0.02655 *  
## ComImportminor         -0.175389   0.112620  -1.557  0.11939    
## MonitArea_km2           0.027645   0.002869   9.636  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Final4 <- glmmTMB(KUD95 ~ LengthStd + Vulnerability + Troph + Habitat + Migration + ComImport + MonitArea_km2 + (1|Transmitter) + (1|Species), data = week_kuds, family = Gamma(link="log"))
summary(Final4)
##  Family: Gamma  ( log )
## Formula:          
## KUD95 ~ LengthStd + Vulnerability + Troph + Habitat + Migration +  
##     ComImport + MonitArea_km2 + (1 | Transmitter) + (1 | Species)
## Data: week_kuds
## 
##      AIC      BIC   logLik deviance df.resid 
##   9941.4  10047.3  -4957.7   9915.4    25599 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.08383  0.2895  
##  Species     (Intercept) 0.02009  0.1417  
## Number of obs: 25612, groups:  Transmitter, 850; Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0753 
## 
## Conditional model:
##                         Estimate Std. Error z value Pr(>|z|)    
## (Intercept)            -0.146276   0.345919  -0.423  0.67239    
## LengthStd               0.221190   0.107616   2.055  0.03984 *  
## Vulnerability          -0.005991   0.003856  -1.554  0.12022    
## Troph                   0.078409   0.106094   0.739  0.45988    
## Habitatdemersal        -0.071999   0.097259  -0.740  0.45913    
## Habitatpelagic-neritic  0.423247   0.135310   3.128  0.00176 ** 
## Migrationoceanodromous  0.130083   0.123292   1.055  0.29139    
## ComImportmedium        -0.162958   0.070556  -2.310  0.02091 *  
## ComImportminor         -0.187196   0.112550  -1.663  0.09627 .  
## MonitArea_km2           0.027935   0.002840   9.837  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Final5 <- glmmTMB(KUD95 ~ LengthStd + Vulnerability + Habitat + Migration + ComImport + MonitArea_km2 + (1|Transmitter) + (1|Species), data = week_kuds, family = Gamma(link="log"))
summary(Final5)
##  Family: Gamma  ( log )
## Formula:          
## KUD95 ~ LengthStd + Vulnerability + Habitat + Migration + ComImport +  
##     MonitArea_km2 + (1 | Transmitter) + (1 | Species)
## Data: week_kuds
## 
##      AIC      BIC   logLik deviance df.resid 
##   9939.9  10037.7  -4958.0   9915.9    25600 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.08375  0.2894  
##  Species     (Intercept) 0.02099  0.1449  
## Number of obs: 25612, groups:  Transmitter, 850; Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0753 
## 
## Conditional model:
##                         Estimate Std. Error z value Pr(>|z|)    
## (Intercept)             0.062773   0.199574   0.315   0.7531    
## LengthStd               0.215352   0.107531   2.003   0.0452 *  
## Vulnerability          -0.004267   0.003137  -1.360   0.1737    
## Habitatdemersal        -0.089431   0.096039  -0.931   0.3518    
## Habitatpelagic-neritic  0.478812   0.115393   4.149 3.33e-05 ***
## Migrationoceanodromous  0.102495   0.119771   0.856   0.3921    
## ComImportmedium        -0.159660   0.071631  -2.229   0.0258 *  
## ComImportminor         -0.162555   0.109263  -1.488   0.1368    
## MonitArea_km2           0.027990   0.002844   9.843  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Final6 <- glmmTMB(KUD95 ~ LengthStd + Vulnerability + Habitat + ComImport + MonitArea_km2 + (1|Transmitter) + (1|Species), data = week_kuds, family = Gamma(link="log"))
summary(Final6)
##  Family: Gamma  ( log )
## Formula:          
## KUD95 ~ LengthStd + Vulnerability + Habitat + ComImport + MonitArea_km2 +  
##     (1 | Transmitter) + (1 | Species)
## Data: week_kuds
## 
##      AIC      BIC   logLik deviance df.resid 
##   9938.6  10028.3  -4958.3   9916.6    25601 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.08374  0.2894  
##  Species     (Intercept) 0.02207  0.1486  
## Number of obs: 25612, groups:  Transmitter, 850; Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0753 
## 
## Conditional model:
##                         Estimate Std. Error z value Pr(>|z|)    
## (Intercept)             0.058174   0.203120   0.286   0.7746    
## LengthStd               0.209921   0.107650   1.950   0.0512 .  
## Vulnerability          -0.003275   0.002981  -1.099   0.2718    
## Habitatdemersal        -0.136348   0.080599  -1.692   0.0907 .  
## Habitatpelagic-neritic  0.524241   0.105321   4.978 6.44e-07 ***
## ComImportmedium        -0.158326   0.073068  -2.167   0.0302 *  
## ComImportminor         -0.149920   0.110231  -1.360   0.1738    
## MonitArea_km2           0.027880   0.002847   9.793  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Final7 <- glmmTMB(KUD95 ~ LengthStd + Habitat + ComImport + MonitArea_km2 + (1|Transmitter) + (1|Species), data = week_kuds, family = Gamma(link="log"))
summary(Final7)
##  Family: Gamma  ( log )
## Formula:          
## KUD95 ~ LengthStd + Habitat + ComImport + MonitArea_km2 + (1 |  
##     Transmitter) + (1 | Species)
## Data: week_kuds
## 
##      AIC      BIC   logLik deviance df.resid 
##   9937.8  10019.3  -4958.9   9917.8    25602 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.08362  0.2892  
##  Species     (Intercept) 0.02394  0.1547  
## Number of obs: 25612, groups:  Transmitter, 850; Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0753 
## 
## Conditional model:
##                         Estimate Std. Error z value Pr(>|z|)    
## (Intercept)            -0.147187   0.082273  -1.789   0.0736 .  
## LengthStd               0.209248   0.108037   1.937   0.0528 .  
## Habitatdemersal        -0.119649   0.081491  -1.468   0.1420    
## Habitatpelagic-neritic  0.509418   0.107251   4.750 2.04e-06 ***
## ComImportmedium        -0.157843   0.075486  -2.091   0.0365 *  
## ComImportminor         -0.094148   0.100718  -0.935   0.3499    
## MonitArea_km2           0.028404   0.002818  10.079  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Final8 <- glmmTMB(KUD95 ~ Habitat + ComImport + MonitArea_km2 + (1|Transmitter) + (1|Species), data = week_kuds, family = Gamma(link="log"))
summary(Final8)
##  Family: Gamma  ( log )
## Formula:          
## KUD95 ~ Habitat + ComImport + MonitArea_km2 + (1 | Transmitter) +  
##     (1 | Species)
## Data: week_kuds
## 
##      AIC      BIC   logLik deviance df.resid 
##   9939.6  10012.9  -4960.8   9921.6    25603 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.08380  0.2895  
##  Species     (Intercept) 0.02207  0.1486  
## Number of obs: 25612, groups:  Transmitter, 850; Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0753 
## 
## Conditional model:
##                         Estimate Std. Error z value Pr(>|z|)    
## (Intercept)            -0.073815   0.070812  -1.042   0.2972    
## Habitatdemersal        -0.078908   0.076210  -1.035   0.3005    
## Habitatpelagic-neritic  0.498507   0.103783   4.803 1.56e-06 ***
## ComImportmedium        -0.143869   0.072709  -1.979   0.0479 *  
## ComImportminor         -0.110944   0.097446  -1.139   0.2549    
## MonitArea_km2           0.029416   0.002759  10.660  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(Final1, Final8) #as there is no evidence that this two models are statistically different from each other, we must choose the simplest one, the one with less variables (Final7)
## Data: week_kuds
## Models:
## Final8: KUD95 ~ Habitat + ComImport + MonitArea_km2 + (1 | Transmitter) + , zi=~0, disp=~1
## Final8:     (1 | Species), zi=~0, disp=~1
## Final1: KUD95 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## Final1:     Troph + Habitat + Migration + ComImport + ReceiverDensity + , zi=~0, disp=~1
## Final1:     MonitArea_km2 + (1 | Transmitter) + (1 | Species), zi=~0, disp=~1
##        Df    AIC   BIC  logLik deviance  Chisq Chi Df Pr(>Chisq)
## Final8  9 9939.6 10013 -4960.8   9921.6                         
## Final1 16 9946.2 10077 -4957.1   9914.2 7.3643      7      0.392
ranef(Final8) #deviation from intercept for each Transmitter and File group
## $Transmitter
##                  (Intercept)
## 1               3.730666e-02
## 10              1.408421e-01
## 11             -5.158580e-02
## 12             -1.396163e-01
## 123             1.999221e-01
## 1248320         9.201090e-02
## 1248321         8.890330e-02
## 1248322         8.905990e-02
## 1248323         9.691870e-02
## 1248324         8.638792e-02
## 1248325         8.898869e-02
## 1248326         9.511135e-02
## 1248327         8.720420e-02
## 1248328         1.073753e-01
## 1248329         7.765798e-02
## 1248330         6.743853e-02
## 1248331         7.728658e-02
## 1248332         7.857853e-02
## 1248333         9.680526e-02
## 1248334         7.686209e-02
## 1248335         7.578072e-02
## 13             -1.023664e-01
## 131             2.384767e-01
## 14              6.160487e-02
## 15              1.855878e-01
## 1511           -1.395549e-01
## 1512           -2.112624e-01
## 1513            1.788442e-02
## 1516            2.040794e-01
## 1517            4.409152e-01
## 1519           -7.057634e-02
## 1521            4.738233e-02
## 1522            2.427593e-01
## 1523            1.309070e-01
## 1524            1.110075e-01
## 1527            1.379043e-01
## 1528           -2.087438e-01
## 1530            5.030706e-02
## 1531            1.132187e-01
## 1532            2.763292e-01
## 1533           -3.820427e-04
## 1534           -7.755792e-02
## 1536            5.639553e-02
## 1537           -1.451874e-01
## 1538           -2.463689e-01
## 1539           -1.649962e-01
## 1540            2.770685e-01
## 1541           -6.484646e-02
## 1542           -1.732022e-02
## 1543            1.838674e-01
## 1544            2.832193e-01
## 1545           -1.253709e-01
## 1546           -1.789529e-02
## 1547           -1.145312e-01
## 1549           -1.528944e-02
## 1551            1.303303e-01
## 1552            5.858525e-02
## 1553           -1.776662e-01
## 1554           -4.318911e-02
## 1555            5.094726e-02
## 1556            1.489819e-01
## 1558            1.793023e-01
## 1559           -2.055727e-01
## 1560           -1.005293e-01
## 1561            2.402106e-01
## 1562            1.727051e-01
## 1563           -1.403621e-01
## 1565           -1.764712e-01
## 1566           -3.553860e-02
## 1568           -2.213847e-01
## 1569            2.256574e-01
## 1570            9.913966e-02
## 1571           -7.681880e-05
## 1574            1.243738e-01
## 1575            2.175807e-01
## 1576           -4.449645e-02
## 1577            1.205541e-01
## 1578            1.030970e-01
## 1579            1.410810e-01
## 1580            9.376943e-02
## 1581            1.493544e-01
## 1582            3.592744e-02
## 1583            5.372606e-01
## 1584           -1.536154e-02
## 1587           -4.237378e-02
## 1590           -2.342161e-01
## 16             -1.269590e-01
## 1606            1.344616e-01
## 1607            3.265211e-01
## 1608           -1.389630e-01
## 1611           -3.706346e-03
## 1613            2.350065e-02
## 1617            1.975495e-01
## 1618           -2.374916e-01
## 1619           -1.022958e-01
## 1620           -1.625800e-01
## 1621            5.586538e-02
## 1623            1.123221e-01
## 1624            8.657740e-03
## 17              7.834459e-02
## 18              4.262193e-01
## 19              9.024059e-02
## 1917           -3.373171e-01
## 1919           -3.032786e-01
## 1921           -3.044572e-01
## 1922           -8.003618e-02
## 1924           -8.026522e-02
## 1925           -3.125758e-01
## 1927           -1.675493e-01
## 1928            1.016761e-01
## 1929           -1.760200e-01
## 1930           -2.096401e-01
## 1931           -2.350041e-03
## 1932           -6.932452e-02
## 1933            1.793695e-01
## 1934           -2.950734e-01
## 1935            2.570324e-01
## 1936            3.242797e-01
## 1937           -3.145362e-01
## 1938           -4.553419e-02
## 1939           -1.109812e-01
## 1940           -1.878120e-01
## 1941           -1.483543e-01
## 1942           -2.999185e-01
## 1943            8.021101e-02
## 1944           -1.352948e-01
## 1946           -2.696013e-01
## 1947           -5.242181e-02
## 1949            1.181958e-01
## 1950           -6.068197e-02
## 1951           -2.951881e-01
## 1952           -6.618027e-02
## 1953            1.463732e-01
## 1954           -3.187464e-02
## 1955           -3.258819e-01
## 1957           -7.948709e-02
## 1958            3.527784e-01
## 1964           -2.520706e-01
## 1966           -6.046837e-02
## 1968           -3.480286e-01
## 1972           -3.371829e-01
## 1973           -2.936894e-01
## 1975           -2.055258e-01
## 1976           -1.834816e-01
## 1977           -3.276840e-01
## 1978           -3.391666e-01
## 1979            2.388293e-01
## 1980           -2.527912e-01
## 1981            2.256731e-01
## 1982           -1.665089e-01
## 1985           -8.706441e-02
## 1986            2.222709e-01
## 1987           -2.309891e-01
## 1988           -3.494839e-01
## 1989           -3.043688e-02
## 1991           -3.479834e-01
## 1992            4.933552e-01
## 1994            2.964646e-02
## 2               2.022795e-01
## 20              1.063361e-01
## 21             -8.901872e-02
## 22              6.554518e-02
## 220            -1.586465e-01
## 23             -4.564616e-02
## 24             -2.218675e-02
## 241            -6.081337e-03
## 25              2.900425e-01
## 26              3.893796e-02
## 27             -7.808345e-02
## 28              4.871772e-02
## 29              2.508972e-01
## 3               4.142334e-02
## 30              1.627329e-01
## 31              3.252387e-01
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## A69-9007-16272  1.902048e-02
## A69-9007-16273 -1.428333e-01
## A69-9007-16274 -3.996868e-01
## A9001          -1.208637e-01
## A9002           2.888550e-02
## A9004          -3.669999e-02
## A9006          -1.171155e-02
## A9007          -1.780227e-01
## A9008           1.908655e-01
## A9009           6.586924e-02
## A9010           2.437952e-01
## A9011           8.426224e-02
## A9012           9.480480e-02
## A9013          -6.811116e-02
## A9014           2.759808e-01
## A9015          -9.070818e-02
## A9016          -4.672469e-02
## A9017           1.256212e-01
## A9018          -5.950900e-02
## A9019           1.046002e-01
## A9020           3.646085e-01
## A9021           1.485266e-01
## A9023           1.874576e-01
## A9025          -8.468654e-02
## A9026          -3.077523e-02
## A9027          -1.768733e-01
## A9028           2.838453e-01
## A9030           3.782546e-01
## A9031           3.605780e-02
## A9032           1.685161e-01
## A9033           1.375526e-01
## A9034          -9.068858e-03
## A9035           1.728784e-01
## A9036          -1.176014e-01
## A9039           5.318623e-02
## A9040           2.136084e-01
## A9041          -6.019323e-02
## A9042           7.318769e-01
## A9043           2.921546e-01
## A9045          -2.794261e-02
## A9046           3.701024e-02
## A9047           3.660829e-01
## A9048           3.404424e-01
## A9049           1.071408e-01
## A9050           5.007363e-01
## A9051           3.295298e-01
## A9052           8.111685e-02
## A9054           4.135049e-01
## A9055           2.069665e-01
## A9056           4.536938e-02
## A9057           1.551700e-01
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## A9060          -7.966569e-02
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## A9063           3.302476e-01
## A9065          -1.225700e-01
## A9071           3.075228e-02
## A9076          -6.048079e-02
## A9083           9.242812e-02
## A9087           2.721237e-02
## A9096           3.207151e-02
## A9100          -1.634706e-01
## G1              7.907074e-02
## G2              6.899022e-02
## G3              6.952219e-02
## G4              1.118596e-01
## ID_18965        7.696265e-03
## ID_18967       -3.436575e-02
## ID_18971       -1.543407e-02
## ID_18972       -3.174229e-01
## ID_18976       -2.210909e-01
## ID_18977       -6.711695e-02
## ID_18978       -7.850899e-02
## ID_18979       -2.951962e-01
## ID_18982        3.625087e-02
## ID_18983a      -3.292225e-02
## ID_18984       -3.315711e-02
## ID_18985       -1.536354e-02
## ID_19051       -5.953441e-02
## ID_19052       -5.437139e-02
## ID_19053       -2.167598e-01
## ID_19054       -3.048500e-01
## ID_19055       -2.156357e-01
## ID_19057       -2.015847e-01
## ID_19059       -6.811839e-02
## ID_9839         1.226561e-01
## ID_9840        -4.735815e-02
## ID_9841         2.406841e-01
## ID_9842         9.478650e-02
## ID_9843         1.248802e-01
## ID_9845         1.318605e-01
## ID_9846         1.890022e-01
## ID_9847         7.807753e-03
## ID_9849         4.645325e-02
## ID_9850        -9.753205e-03
## ID_9851         1.765140e-01
## ID_9852         2.475798e-01
## ID_9853        -3.553441e-02
## ID_9854         4.931309e-02
## ID_9856        -3.157762e-02
## ID_9857         2.285877e-03
## ID_9859         7.299104e-05
## ID_9860         6.439113e-03
## ID_9861         4.677510e-02
## ID_9862         1.450491e-01
## ID_9864         1.224690e-01
## ID_9865         5.000763e-02
## ID_9866         3.628821e-02
## Linguado 01     2.518343e-01
## Linguado 02    -1.896091e-01
## Linguado 03    -2.955544e-01
## Linguado 04     3.522018e-01
## Linguado 05     4.834668e-03
## Linguado 06    -1.789132e-01
## Linguado 07     3.215672e-01
## Linguado 08     2.337850e-01
## Linguado 09    -7.145035e-02
## Linguado 10    -6.127841e-02
## Linguado 11    -2.055534e-01
## Linguado 12    -1.705411e-01
## Linguado 13    -2.841531e-01
## Linguado 14    -2.858375e-01
## Linguado 15     2.493775e-01
## Linguado 16     3.166448e-01
## Linguado 17    -1.747327e-01
## Linguado 18     4.086409e-02
## Linguado 19    -7.347392e-04
## Linguado 20    -7.616439e-03
## Linguado 21     2.944711e-01
## Linguado 22     2.164418e-01
## Sargo 02        2.190946e-01
## Sargo 03       -1.974332e-01
## Sargo 05        2.307576e-01
## Sargo 06       -4.374312e-02
## Sargo 07       -2.151241e-01
## Sargo 08        2.400650e-01
## Sargo 09       -1.364617e-01
## Sargo 12       -1.475832e-01
## Sargo 13       -1.065831e-01
## Sargo 14       -6.740131e-02
## Sargo 15       -1.370531e-01
## Sargo 16       -1.337900e-01
## Sargo 17        5.739353e-02
## Sargo 18        5.843953e-01
## Sargo 19        6.604623e-01
## Sargo 20       -1.017104e-01
## 
## $Species
##       (Intercept)
## Dcer  0.026737650
## Dden  0.114978034
## Dlab -0.023096876
## Dsar  0.033274574
## Dvol  0.015474584
## Dvul  0.224718361
## Emar -0.221201055
## Gmor -0.060379869
## Lami  0.122570172
## Lber  0.067634262
## Lmor -0.018997664
## Pden  0.106755940
## Pery  0.123786622
## Ppag -0.147471065
## Psal  0.139557128
## Satr -0.150093272
## Saur  0.194177424
## Scab  0.109644450
## Scan -0.169957174
## Scre -0.153287556
## Scro -0.124987058
## Sdum  0.120027662
## Spor  0.031925206
## Sriv -0.270398518
## Sscr -0.139746182
## Ssen  0.093771136
## Sumb  0.010807102
## Svir -0.116730126
## Ucir -0.029827065
## Xnov  0.007876212
confint(Final8)
##                                       2.5 %       97.5 %    Estimate
## (Intercept)                     -0.21260365  0.064973900 -0.07381488
## Habitatdemersal                 -0.22827732  0.070461578 -0.07890787
## Habitatpelagic-neritic           0.29509530  0.701918216  0.49850676
## ComImportmedium                 -0.28637700 -0.001361821 -0.14386941
## ComImportminor                  -0.30193384  0.080046730 -0.11094355
## MonitArea_km2                    0.02400742  0.034823897  0.02941566
## Std.Dev.(Intercept)|Transmitter  0.27407185  0.305746568  0.28947630
## Std.Dev.(Intercept)|Species      0.10551320  0.209160661  0.14855710
#evaluate if the final model is better than the ones including only one biological trait
anova(glmmTMB(KUD95 ~ Habitat + (1|Transmitter) + (1|Species), data=week_kuds, family=Gamma(link="log")), Final8)
## Data: week_kuds
## Models:
## glmmTMB(KUD95 ~ Habitat + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log")): KUD95 ~ Habitat + (1 | Transmitter) + (1 | Species), zi=~0, disp=~1
## Final8: KUD95 ~ Habitat + ComImport + MonitArea_km2 + (1 | Transmitter) + , zi=~0, disp=~1
## Final8:     (1 | Species), zi=~0, disp=~1
##                                                                                                              Df
## glmmTMB(KUD95 ~ Habitat + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log"))  6
## Final8                                                                                                        9
##                                                                                                                  AIC
## glmmTMB(KUD95 ~ Habitat + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log")) 10038.6
## Final8                                                                                                        9939.6
##                                                                                                                BIC
## glmmTMB(KUD95 ~ Habitat + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log")) 10088
## Final8                                                                                                       10013
##                                                                                                               logLik
## glmmTMB(KUD95 ~ Habitat + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log")) -5013.3
## Final8                                                                                                       -4960.8
##                                                                                                              deviance
## glmmTMB(KUD95 ~ Habitat + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log"))  10026.6
## Final8                                                                                                         9921.6
##                                                                                                               Chisq
## glmmTMB(KUD95 ~ Habitat + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log"))       
## Final8                                                                                                       104.99
##                                                                                                              Chi Df
## glmmTMB(KUD95 ~ Habitat + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log"))       
## Final8                                                                                                            3
##                                                                                                              Pr(>Chisq)
## glmmTMB(KUD95 ~ Habitat + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log"))           
## Final8                                                                                                        < 2.2e-16
##                                                                                                                 
## glmmTMB(KUD95 ~ Habitat + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log"))    
## Final8                                                                                                       ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(glmmTMB(KUD95 ~ Migration + (1|Transmitter) + (1|Species), data=week_kuds, family=Gamma(link="log")), Final8)
## Data: week_kuds
## Models:
## glmmTMB(KUD95 ~ Migration + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log")): KUD95 ~ Migration + (1 | Transmitter) + (1 | Species), zi=~0, disp=~1
## Final8: KUD95 ~ Habitat + ComImport + MonitArea_km2 + (1 | Transmitter) + , zi=~0, disp=~1
## Final8:     (1 | Species), zi=~0, disp=~1
##                                                                                                                Df
## glmmTMB(KUD95 ~ Migration + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log"))  5
## Final8                                                                                                          9
##                                                                                                                    AIC
## glmmTMB(KUD95 ~ Migration + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log")) 10048.8
## Final8                                                                                                          9939.6
##                                                                                                                  BIC
## glmmTMB(KUD95 ~ Migration + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log")) 10090
## Final8                                                                                                         10013
##                                                                                                                 logLik
## glmmTMB(KUD95 ~ Migration + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log")) -5019.4
## Final8                                                                                                         -4960.8
##                                                                                                                deviance
## glmmTMB(KUD95 ~ Migration + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log"))  10038.8
## Final8                                                                                                           9921.6
##                                                                                                                 Chisq
## glmmTMB(KUD95 ~ Migration + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log"))       
## Final8                                                                                                         117.22
##                                                                                                                Chi Df
## glmmTMB(KUD95 ~ Migration + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log"))       
## Final8                                                                                                              4
##                                                                                                                Pr(>Chisq)
## glmmTMB(KUD95 ~ Migration + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log"))           
## Final8                                                                                                          < 2.2e-16
##                                                                                                                   
## glmmTMB(KUD95 ~ Migration + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log"))    
## Final8                                                                                                         ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

We reached the best fitted model (Final8). Now we must analyse the residuals to see how they behave.

#Test residuals for the best model (goodness of fit)
testDispersion(Final8)  #plot with normality, dispersion and outliers
## 
##  DHARMa nonparametric dispersion test via sd of residuals fitted vs.
##  simulated
## 
## data:  simulationOutput
## dispersion = 1.6378, p-value < 2.2e-16
## alternative hypothesis: two.sided
simulationOutput <- simulateResiduals(fittedModel = Final8, plot = F) #dispersion test
testDispersion(simulationOutput) #dispersion test

## 
##  DHARMa nonparametric dispersion test via sd of residuals fitted vs.
##  simulated
## 
## data:  simulationOutput
## dispersion = 1.6378, p-value < 2.2e-16
## alternative hypothesis: two.sided
plot(simulationOutput) #residual analysis

plotQQunif(simulationOutput) #Q-Q plot (normality checking)

plotResiduals(simulationOutput) #residual vs. predicted (homoscedasticity checking)

testOutliers(simulationOutput) #outliers checking

## 
##  DHARMa outlier test based on exact binomial test with approximate
##  expectations
## 
## data:  simulationOutput
## outliers at both margin(s) = 266, observations = 25612, p-value =
## 2.836e-05
## alternative hypothesis: true probability of success is not equal to 0.007968127
## 95 percent confidence interval:
##  0.009180442 0.011704012
## sample estimates:
## frequency of outliers (expected: 0.00796812749003984 ) 
##                                             0.01038576
#Simulations from the model
getObservedResponse(Final8)  #response used to fit the model
##     [1]  1.079  0.839  3.494  0.819  1.162  0.764  0.893  1.385  1.284  1.772
##    [11]  1.433  1.812  2.506  1.990  0.762  0.762  0.879  0.990  1.074  1.063
##    [21]  1.078  1.020  1.103  0.909  1.131  0.865  0.864  0.874  0.898  0.873
##    [31]  1.736  1.943  1.911  1.812  1.923  1.906  1.931  1.969  1.818  1.800
##    [41]  1.884  1.790  1.642  1.577  1.780  1.762  1.762  1.803  1.782  1.871
##    [51]  1.819  0.929  1.496  1.629  1.738  1.714  1.779  1.851  1.842  1.362
##    [61]  1.697  1.587  1.923  1.977  1.733  2.198  2.102  2.254  2.059  1.993
##    [71]  1.755  1.862  1.833  1.751  1.797  1.726  1.699  1.685  1.774  1.814
##    [81]  1.914  1.724  1.840  1.799  0.856  1.668  1.138  1.003  0.902  0.960
##    [91]  0.912  0.840  0.899  0.903  0.913  0.916  1.003  0.931  0.836  0.840
##   [101]  0.835  0.795  0.827  0.841  0.829  0.842  0.820  0.818  0.813  1.044
##   [111]  1.117  1.126  1.121  1.096  1.077  1.160  1.012  1.020  1.716  0.983
##   [121]  0.868  0.858  1.061  1.100  0.883  0.885  0.850  1.673  1.012  1.060
##   [131]  0.874  0.862  0.998  1.788  1.553  1.132  1.659  1.383  0.919  0.835
##   [141]  0.924  0.956  0.955  0.839  1.104  1.002  1.093  1.125  1.072  1.019
##   [151]  1.011  1.138  1.078  1.072  1.099  1.083  0.962  1.107  1.078  1.113
##   [161]  1.116  1.278  0.937  1.136  1.134  1.130  1.103  1.118  1.119  1.102
##   [171]  1.121  0.933  0.855  1.259  1.454  1.069  0.912  0.786  0.997  1.621
##   [181]  1.606  1.702  1.469  1.001  1.512  1.652  1.264  1.221  1.552  1.881
##   [191]  0.928  1.161  0.967  1.043  1.138  0.951  1.273  0.818  0.893  0.816
##   [201]  0.918  1.083  1.064  0.918  0.889  1.055  0.961  1.123  0.951  1.128
##   [211]  1.111  1.037  1.106  1.119  1.101  0.949  1.427  1.653  1.950  1.734
##   [221]  1.041  0.937  1.963  2.062  0.924  0.928  1.187  2.061  1.679  1.728
##   [231]  1.763  1.623  1.945  2.114  1.274  1.029  1.531  1.430  1.611  0.938
##   [241]  0.958  0.919  0.856  0.910  0.851  0.805  0.803  0.838  0.920  0.799
##   [251]  0.781  0.782  0.936  0.878  1.037  1.020  1.141  1.177  1.373  1.298
##   [261]  1.230  1.159  1.210  1.188  1.225  1.349  1.124  1.059  0.979  1.912
##   [271]  1.478  1.047  0.982  1.227  1.516  1.508  1.334  1.612  1.280  1.173
##   [281]  1.508  1.366  0.966  1.781  1.802  1.390  1.100  0.911  0.923  0.996
##   [291]  0.818  1.075  0.946  0.980  0.959  1.040  1.117  1.061  1.005  1.113
##   [301]  1.134  1.094  1.076  1.200  1.083  1.057  1.119  1.030  1.037  0.992
##   [311]  1.013  1.051  1.055  1.023  0.938  0.959  1.049  1.120  1.107  1.138
##   [321]  1.166  1.222  1.304  0.794  0.879  0.912  1.146  1.060  1.103  1.457
##   [331]  1.487  1.597  1.793  1.560  1.959  1.751  1.593  1.651  1.028  1.648
##   [341]  1.137  1.148  1.386  1.306  1.435  1.373  1.538  1.389  1.529  1.386
##   [351]  1.502  1.803  1.854  1.887  1.829  2.110  2.254  1.646  1.921  2.071
##   [361]  2.030  1.990  1.820  1.599  2.043  1.736  1.996  2.074  2.948  2.175
##   [371]  1.843  2.186  1.510  1.825  1.847  1.512  1.349  1.623  0.984  0.933
##   [381]  1.253  1.123  1.405  0.948  1.535  0.792  0.790  0.775  0.807  0.771
##   [391]  0.782  0.776  0.769  0.771  0.791  1.307  1.012  0.868  1.220  1.172
##   [401]  0.864  0.845  0.950  1.023  0.969  0.807  0.879  1.169  1.054  0.916
##   [411]  1.289  1.037  1.305  1.052  1.356  1.685  1.732  1.323  1.282  1.253
##   [421]  1.806  1.748  1.215  1.174  1.096  1.403  1.235  1.584  0.897  0.864
##   [431]  0.912  0.829  0.848  0.824  0.803  0.798  0.807  0.970  0.940  0.817
##   [441]  0.824  0.822  0.797  0.801  0.808  0.796  0.870  0.884  0.900  0.901
##   [451]  0.816  0.904  0.937  0.940  1.833  1.672  0.977  0.979  1.051  1.554
##   [461]  0.793  0.831  0.914  0.871  0.847  0.939  0.953  0.899  0.860  0.884
##   [471]  0.908  0.915  0.867  0.836  1.064  0.850  0.863  0.810  0.980  0.870
##   [481]  1.015  0.923  1.050  1.082  1.042  1.041  1.112  1.040  1.022  1.012
##   [491]  0.961  1.032  0.898  0.785  0.829  1.058  1.049  0.905  0.788  0.789
##   [501]  1.784  1.293  1.504  1.516  0.980  1.488  1.827  1.453  1.136  1.815
##   [511]  1.667  1.423  1.564  0.882  1.024  1.042  1.078  0.792  0.812  1.198
##   [521]  1.227  1.228  1.578  1.262  1.117  1.325  1.080  1.150  1.578  1.570
##   [531]  1.509  1.192  1.720  1.520  1.606  1.407  1.252  1.264  1.392  1.353
##   [541]  0.821  1.172  1.328  1.354  1.917  1.520  1.332  1.505  1.495  1.195
##   [551]  1.459  1.257  1.298  1.074  1.277  1.266  1.222  1.120  1.471  1.298
##   [561]  1.300  1.172  1.374  1.229  1.178  1.183  1.311  1.176  1.209  1.148
##   [571]  1.181  1.648  0.820  0.800  0.801  0.788  0.797  0.800  0.808  0.809
##   [581]  0.786  0.793  0.794  0.794  0.799  0.808  0.804  0.906  0.983  0.939
##   [591]  0.895  0.893  0.819  0.795  0.834  0.791  0.795  0.785  0.800  0.893
##   [601]  0.782  0.773  0.800  0.971  0.772  0.770  0.776  0.782  0.796  0.784
##   [611]  0.861  0.767  0.823  0.771  0.783  0.800  0.802  0.924  0.803  0.799
##   [621]  0.821  0.791  0.810  0.805  0.793  1.081  1.066  1.094  1.102  1.066
##   [631]  1.132  1.074  1.092  1.121  1.156  1.111  1.114  1.083  1.143  1.102
##   [641]  1.159  1.124  1.169  1.293  1.341  1.360  1.340  1.164  1.033  1.166
##   [651]  1.230  1.358  1.294  1.302  1.567  1.338  1.318  1.270  1.243  1.183
##   [661]  1.476  1.362  1.350  1.227  1.201  1.229  1.226  1.149  1.142  1.378
##   [671]  0.842  1.247  1.120  1.142  1.088  1.089  0.825  0.794  0.827  0.794
##   [681]  0.834  0.890  1.146  0.807  0.790  0.798  0.810  0.801  0.915  0.794
##   [691]  0.793  0.826  0.823  0.799  0.849  1.262  1.639  0.784  1.391  1.830
##   [701]  1.669  1.432  1.491  0.802  0.783  0.879  0.829  0.802  0.789  1.356
##   [711]  1.359  1.358  1.275  1.311  1.316  1.341  1.710  1.299  1.196  1.537
##   [721]  1.658  1.686  1.190  1.413  1.537  1.576  1.309  1.142  1.431  1.566
##   [731]  1.650  1.246  0.983  1.213  1.288  1.420  1.486  1.516  1.163  1.411
##   [741]  1.341  1.353  1.226  1.527  1.327  1.322  1.541  1.491  1.442  1.525
##   [751]  1.271  1.068  1.128  1.629  1.797  1.642  1.285  1.459  1.786  1.494
##   [761]  1.735  1.736  1.705  1.679  1.314  1.364  1.604  1.620  1.654  1.694
##   [771]  1.752  1.825  1.719  1.522  1.848  1.710  1.457  1.441  1.630  1.880
##   [781]  1.862  1.816  0.880  1.535  1.834  1.413  1.054  1.721  0.982  1.405
##   [791]  0.824  0.988  1.009  1.137  1.448  1.327  1.469  1.413  1.212  1.428
##   [801]  1.462  1.422  1.252  1.300  1.519  1.468  1.070  0.948  0.926  1.033
##   [811]  1.079  0.955  0.840  0.843  0.875  0.796  1.380  0.939  0.766  0.764
##   [821]  1.330  1.037  1.384  1.940  2.013  1.703  1.880  2.046  2.011  1.764
##   [831]  1.500  1.890  1.996  1.501  1.546  2.650  1.915  1.464  1.350  1.783
##   [841]  1.422  1.911  1.807  1.402  1.183  1.336  1.625  1.705  1.199  1.687
##   [851]  2.112  1.626  1.959  1.773  1.752  2.016  1.840  1.673  1.341  1.148
##   [861]  1.394  1.083  0.857  0.858  2.583  1.746  1.788  1.391  1.767  1.625
##   [871]  1.467  1.423  0.877  1.167  0.761  0.762  0.762  0.762  0.762  1.015
##   [881]  1.123  1.147  1.307  1.277  1.235  1.200  1.286  1.199  1.448  1.339
##   [891]  1.381  1.672  1.310  1.180  1.548  1.501  1.462  1.208  1.507  1.414
##   [901]  1.419  1.495  1.280  1.463  0.762  0.762  0.762  0.761  0.761  0.765
##   [911]  0.763  0.761  0.763  0.766  0.761  0.765  0.762  0.761  0.761  0.761
##   [921]  0.762  0.761  0.761  0.761  0.792  0.761  2.018  1.115  0.847  0.965
##   [931]  1.270  1.032  1.309  1.631  1.245  1.355  1.385  1.173  1.299  0.802
##   [941]  0.769  1.195  0.936  0.764  0.771  0.762  0.764  0.762  0.761  0.761
##   [951]  0.762  0.762  0.761  0.761  0.766  0.929  0.929  1.006  1.019  0.950
##   [961]  1.620  1.587  1.661  1.777  1.652  1.582  0.867  1.094  1.653  2.291
##   [971]  2.184  2.684  2.598  1.782  0.776  1.723  0.869  0.821  1.863  1.561
##   [981]  1.742  1.348  1.415  1.623  1.732  1.820  1.722  1.850  2.007  1.863
##   [991]  1.962  1.820  1.859  1.988  2.412  0.761  1.761  1.655  1.063  1.189
##  [1001]  0.949  1.010  3.857  1.045  1.243  1.267  1.283  1.063  1.320  1.008
##  [1011]  1.348  1.335  1.352  1.281  1.295  1.263  1.209  1.264  1.137  1.028
##  [1021]  0.897  1.046  1.048  1.161  1.068  1.335  1.326  1.312  1.350  1.240
##  [1031]  1.696  2.186  2.458  0.761  2.594  1.289  1.153  1.233  1.358  1.258
##  [1041]  1.334  1.336  1.351  1.321  1.252  1.169  0.766  0.766  0.761  0.766
##  [1051]  0.762  0.761  0.761  0.761  0.761  0.761  0.761  0.762  4.133  3.133
##  [1061]  0.763  0.761  0.761  0.762  0.762  0.762  0.761  0.762  0.765  0.761
##  [1071]  0.766  0.762  0.761  0.762  0.761  0.761  0.761  0.761  0.761  0.761
##  [1081]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
##  [1091]  0.761  0.763  0.761  0.763  0.761  0.762  0.761  1.567  1.713  1.394
##  [1101]  1.585  0.762  1.573  1.312  1.335  0.898  1.456  0.806  0.826  0.839
##  [1111]  0.948  0.952  1.157  1.193  1.166  1.575  2.087  1.509  2.145  1.829
##  [1121]  2.255  1.498  2.222  1.760  2.122  2.226  2.227  2.248  2.205  2.066
##  [1131]  2.026  1.794  1.717  1.881  1.993  2.109  8.410  7.846  8.805  4.991
##  [1141]  3.834  6.338  8.343  6.595  7.410  7.560  6.803  7.530  5.109  5.559
##  [1151]  5.943  1.492  1.689  1.608  1.796  0.946  1.024  1.535  0.901  0.846
##  [1161]  1.620  1.297  1.415  1.588  1.749  1.275  1.054  1.532  1.882  1.158
##  [1171]  1.896  1.722  1.723  2.081  1.995  2.254  1.996  2.209  1.550  2.101
##  [1181]  1.674  1.667  1.710  2.215  1.516  2.192  2.050  1.932  2.043  2.179
##  [1191]  1.910  2.239  1.964  2.132  2.088  1.994  1.780  1.617  1.894  1.793
##  [1201]  1.687  1.499  2.307  1.906  1.819  1.806  1.459  1.925  1.010  1.700
##  [1211]  1.040  1.559  1.155  1.644  1.397  1.657  1.791  7.752  7.273  5.443
##  [1221]  2.245  2.754  1.496  2.823  3.655  7.074  7.297  2.410  1.601  1.642
##  [1231]  2.241  1.344  0.943  1.328  1.328  1.048  1.286  1.209  1.525  1.304
##  [1241]  1.098  1.445  1.052  1.681  1.442  1.669  1.765  1.081  1.165  1.429
##  [1251]  1.352  1.845  1.781  1.587  1.798  2.282  2.091  2.135  1.883  1.740
##  [1261]  1.844  1.747  1.659  1.516  1.819  1.826  1.748  1.761  1.779  1.619
##  [1271]  1.959  2.447  1.864  2.127  2.095  1.472  1.705  2.113  1.353  1.848
##  [1281]  1.058  1.857  1.120  1.042  1.304  1.038  1.756  1.016  1.517  1.041
##  [1291]  1.268  1.162  1.451  1.703  1.567  1.456  1.230  1.071  1.481  1.076
##  [1301]  1.601  1.389  2.114  2.345  2.211  2.835  3.964  4.869  2.149  2.740
##  [1311]  1.866  1.629  2.017  1.116  0.868  1.265  1.006  1.415  1.031  1.109
##  [1321]  1.083  1.302  1.334  0.914  0.978  1.148  1.365  1.361  1.522  1.421
##  [1331]  1.561  1.034  1.142  1.023  1.368  1.387  1.425  1.926  1.353  1.989
##  [1341]  1.929  1.787  1.932  1.717  1.104  1.477  1.329  1.820  1.283  1.489
##  [1351]  1.318  1.576  1.553  1.363  2.196  1.534  2.081  1.384  1.312  1.280
##  [1361]  1.091  0.828  1.322  1.192  1.372  1.388  1.413  1.291  1.356  1.260
##  [1371]  1.375  1.497  1.355  1.257  1.961  1.275  0.944  0.967  1.114  0.822
##  [1381]  0.862  1.068  1.708  1.787  1.947  1.828  1.726  1.726  1.640  1.593
##  [1391]  1.415  1.454  1.007  0.761  0.761  0.762  0.762  0.761  1.118  0.761
##  [1401]  0.762  0.861  1.252  0.761  0.761  0.762  0.761  0.764  0.762  0.761
##  [1411]  0.761  0.761  1.162  0.761  0.826  0.761  0.761  0.761  0.761  0.761
##  [1421]  0.761  0.761  0.761  0.761  0.762  0.762  0.762  0.761  0.761  0.877
##  [1431]  0.761  0.762  0.849  1.333  0.761  0.761  0.766  0.762  0.761  0.764
##  [1441]  0.762  0.761  0.764  1.285  0.761  0.761  1.325  1.009  0.761  0.761
##  [1451]  0.968  0.761  0.761  0.761  0.761  0.761  0.761  0.761  1.233  1.276
##  [1461]  1.411  0.764  0.761  0.762  0.761  0.761  1.088  0.761  0.761  0.761
##  [1471]  0.761  0.761  0.761  0.761  1.528  0.764  0.761  1.260  0.762  0.761
##  [1481]  0.761  1.044  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
##  [1491]  0.761  0.761  1.375  1.527  2.818  1.516  1.310  1.263  0.761  1.516
##  [1501]  0.761  1.517  1.517  2.257  2.018  0.761  2.932  3.674  2.695  1.452
##  [1511]  3.017  1.376  1.517  2.218  1.516  1.526  0.761  1.524  0.762  0.762
##  [1521]  0.761  0.761  2.412  1.161  3.188  1.714  3.432  1.593  3.214  3.654
##  [1531]  0.761  1.736  1.173  0.761  0.761  0.761  0.761  1.097  0.761  0.761
##  [1541]  0.761  1.368  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
##  [1551]  0.761  0.761  0.762  0.762  0.761  0.761  0.761  0.761  0.761  0.761
##  [1561]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.762  0.761  0.762
##  [1571]  0.761  0.762  0.761  0.761  0.761  0.762  1.513  1.194  1.468  0.762
##  [1581]  0.761  0.761  0.761  0.761  0.761  0.761  1.245  1.516  0.761  0.761
##  [1591]  0.766  0.762  0.761  0.901  0.761  0.761  0.761  0.762  0.761  1.526
##  [1601]  0.889  1.431  0.762  0.761  0.761  0.761  0.761  0.761  0.955  1.345
##  [1611]  1.516  0.761  0.761  0.766  0.762  0.761  0.895  0.761  0.761  0.761
##  [1621]  0.761  1.414  1.554  1.725  1.558  1.086  1.195  0.913  1.082  0.762
##  [1631]  0.761  0.762  0.761  0.764  0.761  0.761  0.984  1.414  0.926  0.945
##  [1641]  1.113  1.126  1.047  1.345  1.488  1.717  1.516  1.104  1.183  1.065
##  [1651]  1.007  0.890  0.904  0.764  0.762  0.761  0.761  1.023  0.761  0.761
##  [1661]  0.955  1.333  1.140  0.991  1.042  1.104  0.995  0.761  0.761  0.761
##  [1671]  0.761  1.524  1.809  1.516  1.148  0.766  0.762  0.761  1.091  0.766
##  [1681]  0.762  0.761  1.496  1.439  1.296  1.521  1.411  1.632  1.517  2.391
##  [1691]  1.276  1.257  1.387  0.761  0.762  2.068  2.110  0.763  3.779  2.213
##  [1701]  2.763  1.842  0.766  1.200  1.928  2.254  0.763  3.663  3.694  3.016
##  [1711]  2.175  0.766  0.761  0.761  0.761  0.761  0.764  0.761  0.761  0.761
##  [1721]  1.287  3.733  1.411  2.219  2.174  2.984  2.957  1.926  3.514  2.182
##  [1731]  2.023  3.849  1.464  1.485  3.615  3.768  2.181  1.453  2.203  1.745
##  [1741]  1.340  3.541  3.713  3.418  2.157  2.634  2.261  2.330  1.978  0.761
##  [1751]  0.761  0.764  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
##  [1761]  0.761  0.761  0.761  0.761  0.761  0.764  0.761  0.761  0.761  0.761
##  [1771]  0.761  0.761  0.761  1.376  0.763  0.761  0.761  0.761  0.764  0.761
##  [1781]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
##  [1791]  0.762  0.761  1.311  0.762  1.156  0.761  0.761  0.761  0.761  0.761
##  [1801]  0.761  0.761  0.761  0.761  0.761  1.313  2.478  1.670  0.761  2.204
##  [1811]  1.198  0.761  1.374  3.226  2.163  1.205  2.707  1.699  0.761  1.554
##  [1821]  1.420  1.319  1.009  1.496  0.764  2.753  2.795  1.534  1.398  1.223
##  [1831]  1.499  0.761  0.761  0.764  0.761  0.761  0.761  0.761  0.761  0.761
##  [1841]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.763  0.761  0.761
##  [1851]  0.761  0.764  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
##  [1861]  0.761  0.761  0.761  0.761  0.970  1.171  1.482  0.766  0.762  0.761
##  [1871]  1.041  1.723  1.078  0.762  0.761  1.201  1.062  1.032  1.822  1.892
##  [1881]  1.728  1.765  1.019  0.903  0.839  1.651  1.841  1.628  1.715  1.411
##  [1891]  1.475  2.052  1.378  3.841  1.072  1.325  1.257  1.011  1.306  1.258
##  [1901]  0.761  0.761  0.883  1.036  0.761  0.761  0.836  1.083  1.742  1.213
##  [1911]  1.743  1.731  0.761  0.761  0.761  0.761  0.761  0.761  1.593  1.217
##  [1921]  0.761  0.761  1.482  1.697  1.357  1.497  1.525  1.694  1.636  1.483
##  [1931]  0.761  1.517  1.411  1.704  1.729  1.224  1.716  1.476  0.761  1.517
##  [1941]  1.342  1.725  1.694  1.668  1.742  1.466  2.171  2.124  0.761  1.501
##  [1951]  1.011  0.761  0.761  1.411  1.562  1.387  0.761  0.761  1.513  1.652
##  [1961]  1.475  1.103  0.761  0.761  1.453  1.639  1.572  0.927  0.761  1.517
##  [1971]  0.761  1.400  1.493  0.761  1.310  1.480  0.761  0.761  1.454  1.370
##  [1981]  0.764  0.762  0.761  0.761  1.388  1.442  0.927  1.297  1.494  0.761
##  [1991]  0.761  1.445  1.381  0.764  0.762  2.346  1.919  1.771  1.753  1.679
##  [2001]  1.620  1.666  1.674  1.723  1.672  1.742  1.753  1.691  1.645  1.695
##  [2011]  1.670  0.761  0.761  0.761  1.514  0.761  1.186  0.761  0.761  0.761
##  [2021]  0.761  0.761  0.761  1.535  0.761  0.761  0.761  0.761  1.517  0.761
##  [2031]  1.139  0.761  0.761  0.761  0.761  0.761  0.761  1.532  0.761  1.496
##  [2041]  0.761  0.761  0.761  0.761  0.761  0.760  0.761  0.761  0.761  0.761
##  [2051]  0.764  0.762  1.502  0.761  0.761  0.761  0.761  0.761  0.760  0.761
##  [2061]  0.761  0.761  0.761  0.764  0.762  1.152  1.737  1.647  2.019  1.461
##  [2071]  0.761  1.161  1.739  1.632  1.615  1.400  0.761  0.761  0.760  1.825
##  [2081]  0.832  0.760  0.760  0.760  0.761  0.761  0.760  0.761  0.760  1.803
##  [2091]  0.762  0.760  0.760  0.760  0.761  0.761  0.760  0.761  0.761  0.761
##  [2101]  0.761  0.761  0.761  0.761  0.761  1.326  0.972  0.894  0.893  1.219
##  [2111]  0.790  1.423  1.146  0.914  0.907  0.849  0.768  1.473  1.463  1.339
##  [2121]  1.477  1.499  1.475  1.524  1.508  1.523  1.447  1.353  1.165  1.469
##  [2131]  1.313  1.528  1.412  1.406  1.457  1.354  1.477  1.476  1.457  1.519
##  [2141]  1.495  1.523  1.461  1.386  1.213  1.457  1.339  1.526  1.877  1.490
##  [2151]  2.716  1.813  0.762  0.761  0.761  4.662  0.783  1.149  0.774  1.073
##  [2161]  0.812  1.844  2.007  0.968  0.814  1.411  0.874  1.950  1.488  0.839
##  [2171]  2.564  1.493  1.143  0.761  2.128  1.809  1.855  1.208  0.911  1.260
##  [2181]  1.691  0.822  1.260  1.256  2.799  0.985  0.762  0.761  4.898  0.870
##  [2191]  0.761  1.116  1.427  1.120  2.081  2.024  0.915  0.822  1.383  1.029
##  [2201]  1.862  1.338  0.793  2.253  1.220  1.032  0.761  2.233  1.458  1.218
##  [2211]  1.133  0.769  1.284  1.510  0.909  1.527  1.386  1.524  1.526  1.296
##  [2221]  1.526  1.339  1.457  1.513  0.761  1.363  1.487  1.413  1.535  1.521
##  [2231]  1.474  1.527  1.368  1.526  1.524  1.324  1.525  1.358  1.456  1.516
##  [2241]  0.761  1.369  1.470  1.453  1.531  1.527  1.474  0.763  5.602  0.906
##  [2251]  0.762  1.994  1.996  0.849  1.949  3.564  3.282  0.761  1.803  0.763
##  [2261]  0.860  3.306  2.570  2.866  3.495  1.590  3.759  1.740  0.897  3.517
##  [2271]  3.326  2.138  2.861  1.548  0.761  3.365  4.136  2.711  0.808  0.761
##  [2281]  0.761  0.762  1.819  4.482  2.947  2.276  1.314  3.196  1.902  1.644
##  [2291]  4.300  1.381  1.976  2.350  2.811  1.401  1.313  2.580  2.143  2.310
##  [2301]  2.124  3.354  1.304  2.123  1.903  2.123  2.701  1.344  0.761  2.731
##  [2311]  2.893  0.761  1.584  0.761  5.206  2.251  4.528  3.570  1.529  2.272
##  [2321]  2.751  2.619  1.178  4.502  2.775  4.483  5.689  2.514  7.286  1.729
##  [2331]  1.455  1.374  2.030  2.073  2.116  1.751  1.455  1.527  0.761  0.802
##  [2341]  3.374  1.154  2.435  1.619  3.440  0.762  0.762  4.270  4.847  3.389
##  [2351]  2.024  3.062  0.762  2.459  0.764  2.213  0.762  3.317  1.918  4.377
##  [2361]  1.743  6.218  1.832  3.935  3.502  4.627  2.368  4.496  4.090  4.301
##  [2371]  2.478  0.762  0.767  1.712  2.395  0.871  2.035  2.095  0.761  1.839
##  [2381]  1.440  1.066  1.679  0.761  0.761  0.763  2.505  1.345  0.761  0.761
##  [2391]  0.762  0.762  0.761  0.761  0.761  0.761  0.906  0.761  0.761  1.237
##  [2401]  0.763  1.703  0.817  0.761  0.761  0.761  1.300  0.761  0.761  0.761
##  [2411]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
##  [2421]  0.761  0.762  0.761  0.763  0.761  0.761  0.761  0.761  2.816  1.241
##  [2431]  4.097  0.764  0.761  2.090  1.627  1.526  1.905  1.850  2.109  2.213
##  [2441]  3.372  3.291  3.445  3.413  2.982  1.836  1.906  1.931  2.053  1.857
##  [2451]  1.699  1.745  1.981  1.486  1.465  2.216  1.278  1.282  0.762  0.761
##  [2461]  2.483  1.034  1.163  1.038  6.556  1.254  1.512  1.343  1.315  1.077
##  [2471]  3.120  0.762  0.761  2.911  1.394  6.831  1.241  7.292  0.761  1.931
##  [2481]  1.201  1.817  3.749  2.091  2.894  1.424  1.379  0.761  4.314  3.429
##  [2491]  1.246  2.991  2.512  3.412  0.762  2.822  6.282  2.099  2.991  1.271
##  [2501]  1.982  1.894  1.640  1.848  1.592  1.698  1.824  3.602  1.951  1.676
##  [2511]  2.512  1.472  1.926  3.774  1.701  3.666  3.127  2.247  1.661  1.935
##  [2521]  2.295  1.880  0.764  1.493  3.747  4.277  3.346  4.987  4.750  5.731
##  [2531]  0.763  0.761  1.225  0.761  3.001  2.046  2.365  3.716  1.473  3.190
##  [2541]  4.881  1.336  1.393  3.413  1.250  1.578  1.574  1.403  1.519  1.254
##  [2551]  1.548  1.846  1.662  0.840  1.560  1.520  1.724  1.217  1.848  1.314
##  [2561]  1.191  1.357  1.440  1.611  1.502  1.527  1.501  1.386  1.464  1.541
##  [2571]  1.430  1.585  1.359  1.560  1.015  1.559  0.762  1.417  0.802  1.637
##  [2581]  0.762  1.271  0.762  1.293  0.762  1.561  1.569  0.762  1.543  0.762
##  [2591]  1.613  1.625  1.041  1.663  1.562  1.378  1.523  1.461  1.562  1.489
##  [2601]  1.407  1.481  1.451  1.424  1.547  1.693  1.920  1.825  1.957  1.993
##  [2611]  1.833  1.724  1.726  1.298  1.491  1.569  0.761  0.762  0.765  0.761
##  [2621]  0.761  0.761  0.763  0.761  0.763  0.761  0.761  0.761  0.761  0.761
##  [2631]  0.761  0.762  1.360  1.522  1.189  0.761  0.762  0.761  0.761  0.761
##  [2641]  0.761  4.983  9.142  2.883  3.143  2.938  3.088  2.740  2.557  1.244
##  [2651]  2.253  1.901  1.209  1.587  1.756  1.959  2.011  1.785  1.359  1.813
##  [2661]  1.960  1.343  0.761  0.761  0.761  0.766  0.761  0.762  0.761  0.761
##  [2671]  0.763  0.761  0.766  0.762  0.761  0.765  0.772  0.761  0.762  0.761
##  [2681]  0.856  1.201  0.890  0.787  0.766  0.761  0.778  0.764  1.333  0.761
##  [2691]  0.761  0.760  1.090  0.761  0.762  0.883  0.761  0.761  0.969  0.994
##  [2701]  0.873  1.077  0.855  0.761  0.762  0.762  0.761  0.761  0.761  0.761
##  [2711]  0.761  0.762  0.761  0.761  5.388  6.075  4.080  7.662  9.166  6.857
##  [2721]  6.955  6.223  5.543  8.678 10.692  6.300  9.127  5.232  6.299  4.754
##  [2731] 14.723  5.417 13.452  3.026 13.020  2.517  7.581  4.883  6.159  4.564
##  [2741]  2.584  7.030  3.266  1.220  4.325  3.196  3.482  2.349  2.465  2.893
##  [2751]  2.364  2.563  2.204  1.740  1.649  2.349  3.029  2.432  1.281  1.833
##  [2761]  1.610  1.877  1.676  1.678  1.719  2.304  1.937  1.629  3.234  2.231
##  [2771]  1.840  2.499  3.291  2.769  2.274  3.505  3.080  2.770  3.856  2.325
##  [2781]  2.550  1.968  1.409  5.251  1.598  4.289  6.615  2.360  3.621  3.010
##  [2791]  2.199  3.563  3.469  3.019  3.413  2.553  3.060  2.545  2.630  3.086
##  [2801]  3.400  4.225  3.862  3.509  2.972  2.876  3.812  1.743  3.649  7.300
##  [2811]  3.578  6.265  3.600  4.134  1.492  4.235  6.680  2.677  7.472  5.010
##  [2821]  4.652  4.589  2.969  3.581  3.344  2.251  2.843  3.097  3.553  5.515
##  [2831]  2.485  1.927  4.721  2.309  1.677  1.525  1.686  1.377  1.867  1.409
##  [2841]  1.350  1.302  1.741  1.737  2.428  2.190  2.251  3.297  2.576  2.354
##  [2851]  2.102  2.673  2.101  2.331  3.647  3.661  2.210  4.367  3.398  4.053
##  [2861]  3.764  3.244  3.430  2.425  1.981  3.465  2.070  1.910  3.141  2.196
##  [2871]  4.136  1.959  3.651  0.848  2.527  2.989  0.839  1.346  1.337  1.805
##  [2881]  1.907  2.204  2.283  1.883  1.468  1.362  1.361  1.453  1.482  1.435
##  [2891]  1.387  1.259  1.437  1.368  1.796  1.739 12.571  6.403  3.991  1.286
##  [2901]  1.369  1.425  1.470  1.436  1.354  1.397  1.352  1.308  1.301  1.286
##  [2911]  1.447  0.761  0.762  0.762  0.762  0.762  0.762  0.761  0.761  0.761
##  [2921]  0.761  0.761  0.761  0.761  0.761  1.003  1.076  0.800  1.081  1.504
##  [2931]  1.509  1.395  1.520  0.762  1.458  0.762  1.509  1.486  1.122  0.762
##  [2941]  1.433  1.397  1.408  2.756  2.322  1.474  1.519  1.634  1.030  0.977
##  [2951]  1.396  1.316  1.391  1.531  1.322  1.500  1.501  0.968  1.524  1.473
##  [2961]  1.190  1.407  1.550  1.526  1.497  1.776  1.685  1.683  1.353  1.492
##  [2971]  0.762  1.313  1.475  1.430  1.564  1.258  1.515  1.518  1.146  1.102
##  [2981]  1.454  1.807  0.992  0.761  1.106  1.972  1.087  1.082  0.761  0.761
##  [2991]  0.761  1.834  2.324  2.748  3.038  2.873  0.872  0.887  0.827  0.761
##  [3001]  0.761  0.761  0.796  0.771  0.771  0.771  0.761  0.761  0.761  0.761
##  [3011]  0.805  1.003  1.006  0.975  0.995  1.002  0.818  1.006  0.912  0.940
##  [3021]  0.958  0.988  0.973  0.766  0.964  0.774  0.784  0.893  0.767  0.770
##  [3031]  0.768  0.797  0.796  0.771  0.769  0.766  0.765  0.805  0.818  0.837
##  [3041]  0.818  0.790  0.846  0.770  0.769  0.762  0.761  0.761  0.761  1.021
##  [3051]  1.086  1.079  0.996  0.967  1.038  1.083  1.065  1.908  1.941  1.044
##  [3061]  1.897  1.227  2.058  0.765  0.764  1.068  1.031  1.057  1.064  1.043
##  [3071]  1.075  1.118  1.089  1.361  1.002  1.688  1.165  1.102  1.044  2.150
##  [3081]  1.071  1.061  1.846  1.956  1.932  1.566  1.741  1.013  1.036  0.941
##  [3091]  1.161  1.041  1.030  0.768  0.770  0.762  1.058  0.835  0.768  0.810
##  [3101]  0.821  0.891  0.928  0.875  0.885  0.907  1.302  1.232  1.231  0.819
##  [3111]  0.817  1.390  1.140  0.862  0.771  0.764  0.761  0.763  0.761  0.763
##  [3121]  0.762  0.762  0.761  0.762  0.762  0.764  0.762  0.762  0.762  0.762
##  [3131]  0.762  0.789  0.824  0.761  0.761  0.761  0.763  0.781  0.809  0.772
##  [3141]  0.859  0.771  0.795  0.960  0.978  0.991  0.985  1.002  1.082  1.657
##  [3151]  1.272  0.937  1.456  0.939  1.535  0.784  0.762  0.829  0.761  0.784
##  [3161]  0.761  0.762  0.762  0.768  0.762  0.761  0.762  0.774  0.765  0.762
##  [3171]  0.762  0.762  0.762  0.762  0.761  0.798  0.761  0.761  0.762  0.766
##  [3181]  0.808  0.761  0.864  0.879  0.761  0.763  0.777  0.777  0.767  0.766
##  [3191]  0.855  0.816  0.773  0.761  0.812  0.856  0.859  0.845  0.851  0.843
##  [3201]  0.849  0.862  0.868  0.832  0.848  0.853  0.844  0.834  0.837  0.865
##  [3211]  0.836  0.866  1.967  1.973  2.202  0.780  0.863  0.824  0.871  0.788
##  [3221]  0.835  0.769  0.762  0.762  0.762  0.762  0.761  0.976  0.857  0.761
##  [3231]  0.762  0.761  0.999  0.761  1.031  0.761  1.081  1.082  1.097  0.761
##  [3241]  0.819  0.822  0.827  0.852  0.806  0.840  0.976  0.762  0.867  0.834
##  [3251]  0.853  0.874  1.024  0.979  0.762  0.762  0.846  0.836  0.798  0.761
##  [3261]  0.964  1.116  1.025  2.199  1.493  2.869  0.977  2.814  2.126  2.711
##  [3271]  1.376  2.418  1.316  2.633  1.237  2.495  1.674  2.113  2.059  2.080
##  [3281]  1.955  2.544  2.072  1.294  0.761  1.905  2.230  2.271  0.761  1.336
##  [3291]  0.761  0.761  2.002  1.490  0.761  2.174  2.089  1.237  1.894  2.824
##  [3301]  2.801  2.088  1.891  2.229  1.899  1.349  2.322  2.326  0.761  1.838
##  [3311]  1.956  1.685  2.102  0.928  1.312  0.762  1.306  0.761  0.761  1.212
##  [3321]  0.915  0.761  0.761  0.876  0.761  0.761  0.761  0.761  0.761  1.219
##  [3331]  1.134  1.042  1.228  1.019  0.901  0.894  0.820  1.037  0.940  0.891
##  [3341]  0.854  1.050  1.091  0.904  1.069  1.101  1.088  1.049  0.953  1.884
##  [3351]  1.335  1.000  1.013  1.150  1.242  1.026  0.997  1.075  1.003  0.896
##  [3361]  0.992  0.912  0.921  0.870  0.761  0.763  0.761  0.800  0.886  0.761
##  [3371]  0.799  0.761  0.761  1.241  0.912  0.911  0.948  1.120  0.949  0.875
##  [3381]  0.905  1.023  0.924  1.001  0.984  1.047  1.006  0.988  1.041  1.050
##  [3391]  1.135  1.013  1.175  0.946  1.146  1.247  0.980  1.133  1.183  1.085
##  [3401]  1.173  1.099  1.410  1.056  1.260  0.858  0.950  0.790  0.798  1.049
##  [3411]  1.019  1.207  1.309  1.480  0.980  1.667  1.361  1.433  1.413  1.251
##  [3421]  0.998  1.097  1.262  1.069  0.998  0.887  0.991  0.997  0.995  0.945
##  [3431]  0.926  0.915  0.992  0.827  0.874  0.832  0.810  0.826  0.883  0.763
##  [3441]  0.864  0.761  1.450  1.023  0.761  0.948  1.025  0.914  1.137  0.806
##  [3451]  1.021  0.839  1.254  1.098  0.996  0.971  1.014  1.092  0.983  0.940
##  [3461]  0.998  0.762  1.190  1.027  0.988  0.910  1.019  0.971  0.803  0.933
##  [3471]  0.822  0.842  0.981  0.969  0.903  0.833  0.831  0.761  0.867  0.761
##  [3481]  1.006  0.995  0.977  0.852  1.000  0.761  1.281  1.558  1.350  1.475
##  [3491]  1.552  1.487  1.364  1.110  1.128  1.403  1.368  1.347  1.391  1.379
##  [3501]  1.385  1.096  1.496  1.168  1.624  1.417  1.569  0.998  1.152  1.176
##  [3511]  1.182  1.187  1.132  1.127  1.162  1.162  1.186  1.190  1.212  1.296
##  [3521]  0.965  0.761  0.761  0.761  0.761  0.761  0.761  0.979  0.761  0.761
##  [3531]  0.761  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762
##  [3541]  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762
##  [3551]  0.762  1.191  0.762  0.762  0.762  1.167  0.762  0.762  0.762  0.762
##  [3561]  1.367  0.762  0.762  0.762  1.579  0.762  0.762  1.054  0.762  0.762
##  [3571]  0.921  0.762  1.021  0.762  0.762  0.762  0.762  1.387  0.762  0.762
##  [3581]  0.762  1.347  0.762  0.762  0.762  0.762  0.762  1.122  0.762  0.762
##  [3591]  0.762  1.710  0.762  0.762  1.389  0.762  0.762  0.762  0.823  0.762
##  [3601]  0.762  0.762  0.762  0.762  0.762  0.818  0.762  0.762  0.762  0.762
##  [3611]  0.762  0.762  0.762  0.762  1.217  0.762  0.762  0.762  0.823  0.762
##  [3621]  0.762  0.762  1.115  0.762  0.762  0.762  0.762  0.994  0.762  0.762
##  [3631]  0.813  0.762  0.762  0.762  0.904  0.762  0.762  0.762  0.762  0.762
##  [3641]  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762
##  [3651]  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762
##  [3661]  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762
##  [3671]  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762
##  [3681]  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762
##  [3691]  0.762  0.762  0.762  0.762  1.269  1.088  0.841  0.762  0.762  0.762
##  [3701]  1.048  0.762  0.870  0.762  0.762  0.762  1.201  0.762  1.065  0.762
##  [3711]  1.168  0.762  1.398  0.762  1.237  0.762  1.243  0.762  0.762  1.250
##  [3721]  0.762  0.762  1.049  0.762  0.762  0.762  1.015  0.762  0.762  0.898
##  [3731]  0.762  0.762  0.909  0.762  0.762  0.839  0.762  0.762  0.762  0.875
##  [3741]  0.762  0.762  0.762  0.761  0.762  0.762  0.762  0.762  0.762  0.762
##  [3751]  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762
##  [3761]  0.762  0.762  0.761  0.762  0.762  0.762  0.762  0.762  0.762  0.762
##  [3771]  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762
##  [3781]  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762
##  [3791]  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762
##  [3801]  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762
##  [3811]  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762
##  [3821]  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762
##  [3831]  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  1.069  1.046
##  [3841]  1.024  0.996  1.251  0.937  0.849  0.846  0.823  0.850  0.897  0.786
##  [3851]  0.761  0.761  0.784  0.861  0.926  0.947  0.876  0.837  1.058  1.076
##  [3861]  1.055  0.763  0.815  0.793  0.813  0.774  0.778  0.787  0.904  0.867
##  [3871]  0.765  0.761  0.761  0.761  1.055  1.213  1.157  1.077  0.925  1.103
##  [3881]  1.200  1.528  1.117  1.146  1.160  1.244  1.241  1.039  1.174  1.189
##  [3891]  1.148  1.300  1.053  1.273  1.110  1.084  0.942  0.867  0.983  1.132
##  [3901]  0.939  1.214  0.913  0.785  0.761  0.761  0.847  0.761  0.762  0.804
##  [3911]  0.761  0.761  0.761  0.761  0.980  0.894  1.038  1.397  1.320  1.755
##  [3921]  1.596  1.341  1.113  0.908  0.775  0.869  1.031  1.008  0.979  0.938
##  [3931]  0.988  1.460  1.273  1.432  0.834  1.103  1.103  1.197  1.263  1.242
##  [3941]  1.249  1.286  1.328  1.201  1.215  1.127  1.257  1.286  1.291  1.292
##  [3951]  1.358  1.288  1.236  1.160  1.146  1.107  0.997  0.813  1.386  1.368
##  [3961]  1.265  1.267  1.201  1.137  1.140  1.282  0.858  0.965  1.057  1.173
##  [3971]  0.969  0.838  0.888  0.788  0.761  0.761  0.761  0.761  0.908  0.761
##  [3981]  0.761  1.139  0.917  1.380  0.761  0.761  1.127  1.033  0.761  0.777
##  [3991]  0.768  0.785  0.768  0.765  0.766  0.776  0.786  0.816  0.766  0.767
##  [4001]  0.768  0.801  0.798  0.762  0.762  0.889  0.915  0.770  0.792  0.875
##  [4011]  0.761  0.788  0.834  0.859  0.846  0.846  0.861  0.873  0.822  0.803
##  [4021]  0.814  0.812  0.900  0.857  0.866  0.859  0.968  0.994  0.924  0.871
##  [4031]  0.779  0.859  0.806  0.788  0.834  0.859  0.846  0.846  0.861  0.873
##  [4041]  0.822  0.803  0.814  0.812  0.900  0.837  0.786  0.984  0.886  0.886
##  [4051]  0.761  0.761  0.761  0.761  0.764  0.762  0.761  0.761  0.761  0.761
##  [4061]  0.761  0.761  0.774  0.764  1.244  1.205  1.125  0.781  0.762  0.796
##  [4071]  0.793  0.925  1.013  0.795  0.801  0.819  0.995  0.860  0.877  0.919
##  [4081]  1.029  0.794  1.037  1.117  1.013  0.803  0.845  0.827  0.838  0.772
##  [4091]  0.810  0.936  0.790  0.872  0.996  0.814  0.813  0.794  0.902  0.847
##  [4101]  0.787  0.786  0.852  0.838  0.848  0.833  0.802  0.771  0.838  0.838
##  [4111]  0.829  0.774  0.835  0.763  0.806  0.855  1.181  0.829  0.814  0.762
##  [4121]  0.934  0.761  1.029  0.981  0.935  0.863  0.932  0.924  0.988  0.984
##  [4131]  0.965  1.154  0.980  0.928  0.999  1.024  1.076  0.987  1.015  1.040
##  [4141]  1.028  1.003  0.991  0.904  1.013  1.066  1.088  1.050  1.056  1.031
##  [4151]  1.025  1.069  1.103  1.048  1.015  1.041  1.087  1.020  1.023  1.053
##  [4161]  1.034  1.010  1.040  1.071  1.031  1.024  1.053  1.081  1.047  1.021
##  [4171]  1.025  1.044  1.006  1.247  0.960  1.055  1.043  1.014  1.406  1.326
##  [4181]  1.210  0.980  0.766  0.767  0.863  1.646  1.436  1.583  1.686  1.592
##  [4191]  1.670  1.557  0.996  1.154  1.594  1.058  0.982  1.234  1.266  1.194
##  [4201]  1.177  1.183  1.046  1.023  0.988  0.990  1.046  0.957  0.989  1.010
##  [4211]  0.991  0.963  0.998  1.007  0.968  1.007  1.456  1.445  1.319  1.332
##  [4221]  1.187  0.925  1.058  1.052  1.080  1.171  1.051  1.169  1.153  0.927
##  [4231]  0.833  0.826  0.824  0.815  0.879  0.850  0.801  0.772  0.796  0.850
##  [4241]  0.831  0.851  0.796  0.823  0.899  0.852  0.812  0.808  0.808  0.802
##  [4251]  0.842  0.898  0.857  0.892  0.819  0.882  0.902  0.921  0.917  1.115
##  [4261]  1.029  1.018  1.007  1.001  0.980  1.049  1.560  1.107  1.106  0.989
##  [4271]  1.134  1.038  1.103  1.107  0.953  0.954  1.076  1.086  1.058  1.033
##  [4281]  0.924  1.014  1.080  1.069  1.043  1.072  1.040  1.067  1.093  1.096
##  [4291]  1.115  1.083  1.037  1.191  1.010  1.077  1.025  1.033  1.052  1.028
##  [4301]  1.066  0.974  0.894  0.887  0.962  0.927  0.899  0.990  0.998  1.158
##  [4311]  1.067  1.071  1.079  1.056  1.167  1.211  1.199  1.157  1.527  1.341
##  [4321]  1.106  1.128  1.106  1.162  1.045  1.103  1.148  1.063  1.019  1.015
##  [4331]  1.055  0.993  1.063  0.967  0.882  1.073  1.081  0.985  1.061  1.105
##  [4341]  0.931  0.885  0.927  0.796  0.818  0.761  0.839  1.098  0.809  0.882
##  [4351]  0.986  1.077  0.884  1.047  1.050  1.081  1.059  1.016  1.145  1.133
##  [4361]  1.114  1.087  0.978  1.170  1.238  1.091  1.208  1.016  1.130  1.018
##  [4371]  0.971  1.047  1.063  1.296  1.092  1.094  1.545  1.038  1.149  1.190
##  [4381]  1.045  1.108  0.969  1.070  1.100  1.322  1.512  0.892  1.067  1.078
##  [4391]  1.064  1.079  1.094  1.052  1.104  1.109  1.084  1.140  1.099  1.148
##  [4401]  1.044  0.987  1.097  1.069  1.077  1.096  1.047  1.019  1.080  1.058
##  [4411]  1.078  1.082  1.023  1.031  1.053  1.183  1.149  0.984  1.074  0.895
##  [4421]  0.761  0.761  0.761  0.761  0.761  0.761  0.762  0.836  0.848  0.772
##  [4431]  0.860  0.761  0.761  0.761  0.761  0.762  0.761  0.766  0.929  1.050
##  [4441]  0.973  1.012  1.053  0.982  1.059  1.048  1.060  1.127  1.090  1.065
##  [4451]  1.064  1.090  1.097  1.021  1.024  1.034  1.032  1.059  1.044  0.986
##  [4461]  1.149  1.024  1.010  1.068  0.988  0.936  0.988  0.792  0.761  0.869
##  [4471]  0.761  0.861  1.139  0.859  1.046  1.087  1.145  0.858  0.853  0.897
##  [4481]  1.020  0.957  0.916  0.981  1.155  0.796  0.989  0.966  1.036  0.843
##  [4491]  0.962  0.972  1.063  0.951  0.931  0.853  0.864  0.762  0.817  0.909
##  [4501]  0.864  0.761  0.761  0.761  0.889  0.888  0.961  0.969  0.900  0.793
##  [4511]  0.846  0.862  0.874  0.954  0.918  1.068  1.041  1.148  1.345  1.063
##  [4521]  0.848  1.036  1.027  0.875  0.801  0.860  0.935  0.868  0.844  0.915
##  [4531]  0.830  0.924  0.917  1.071  1.279  1.024  1.001  1.043  0.959  0.895
##  [4541]  0.914  1.015  0.875  0.838  0.845  0.903  1.054  0.921  0.888  0.828
##  [4551]  0.820  0.886  0.987  0.899  1.043  0.772  0.873  0.922  1.187  1.262
##  [4561]  1.119  1.111  1.219  1.180  1.238  1.118  0.964  1.424  1.199  1.609
##  [4571]  1.287  1.686  1.551  1.183  1.272  1.668  1.772  1.436  1.214  0.902
##  [4581]  1.156  1.285  1.212  1.153  1.173  1.165  1.238  1.248  1.243  1.012
##  [4591]  1.162  1.520  1.334  1.367  1.422  1.459  1.367  1.183  1.174  1.421
##  [4601]  1.459  1.431  1.332  1.287  1.358  1.246  1.203  1.209  1.156  1.328
##  [4611]  1.589  1.239  1.267  1.214  1.102  1.113  1.051  0.997  1.037  0.990
##  [4621]  0.992  0.910  1.244  1.001  1.131  1.097  1.320  1.221  1.062  0.847
##  [4631]  0.857  0.987  1.010  0.883  0.861  1.040  0.874  0.950  0.921  0.984
##  [4641]  0.918  0.893  0.878  1.020  1.040  0.970  0.915  0.895  0.941  0.995
##  [4651]  1.089  0.901  0.855  1.047  0.852  1.102  0.906  0.941  0.809  1.107
##  [4661]  0.942  0.911  0.891  1.142  1.168  1.016  1.168  0.938  0.891  1.024
##  [4671]  0.997  0.932  1.026  1.108  1.040  0.987  1.059  0.961  1.032  1.077
##  [4681]  1.435  1.133  1.027  1.300  0.976  1.050  0.856  1.023  0.985  1.040
##  [4691]  0.962  0.948  0.974  0.953  0.881  0.970  0.839  0.886  0.958  1.061
##  [4701]  0.852  1.106  0.962  0.963  1.044  1.167  1.026  1.008  1.012  0.981
##  [4711]  0.892  0.978  0.993  1.042  0.937  1.020  1.124  0.973  1.120  1.161
##  [4721]  1.096  1.220  1.240  1.151  1.155  1.106  1.112  1.147  1.193  1.049
##  [4731]  1.108  1.075  1.191  1.507  1.224  1.147  2.007  1.434  1.293  1.184
##  [4741]  1.056  1.288  1.262  1.347  1.283  1.238  1.436  1.322  1.411  1.314
##  [4751]  1.403  1.287  1.365  1.217  1.267  1.280  1.228  1.246  1.221  1.188
##  [4761]  1.112  1.226  1.164  1.147  1.261  1.202  1.274  1.024  1.315  1.323
##  [4771]  1.068  1.184  1.212  0.921  1.335  1.237  1.118  0.805  1.308  0.961
##  [4781]  0.926  1.184  1.042  1.354  1.167  1.566  1.378  1.115  0.801  1.003
##  [4791]  0.959  1.386  0.761  1.213  1.049  1.182  1.319  1.146  1.021  0.966
##  [4801]  0.930  1.153  1.134  1.029  1.175  1.002  1.174  0.940  0.998  0.941
##  [4811]  1.023  1.112  1.323  1.396  1.276  1.137  1.284  1.234  0.982  1.172
##  [4821]  0.993  1.538  1.308  1.135  1.296  1.140  1.310  1.285  1.288  1.261
##  [4831]  1.276  1.143  1.255  1.148  1.347  1.452  1.678  1.690  1.529  1.638
##  [4841]  1.278  1.379  1.415  1.137  1.877  1.131  1.432  1.386  1.427  1.363
##  [4851]  1.466  1.417  1.354  1.036  1.049  1.543  1.525  1.533  1.519  1.563
##  [4861]  1.546  1.562  1.467  1.570  0.997  1.253  1.199  1.115  1.159  1.209
##  [4871]  1.179  1.210  1.211  1.188  1.396  1.212  0.849  0.765  0.766  0.762
##  [4881]  0.768  0.779  0.779  0.785  0.774  0.776  0.794  0.819  0.858  0.840
##  [4891]  0.792  0.787  0.829  0.957  0.768  0.787  0.774  0.811  0.803  0.774
##  [4901]  0.784  0.767  0.762  0.762  0.913  0.824  0.816  0.786  0.844  0.812
##  [4911]  0.817  0.796  0.785  0.828  0.785  0.819  0.764  0.866  0.879  1.027
##  [4921]  0.973  1.048  0.989  1.017  1.053  0.986  1.017  1.004  1.050  1.035
##  [4931]  1.050  0.985  1.016  1.103  1.020  0.998  1.024  0.985  1.133  1.063
##  [4941]  0.868  1.014  0.945  0.951  1.014  1.069  0.999  1.050  1.071  1.154
##  [4951]  1.108  1.040  1.004  1.040  0.775  1.002  1.019  1.047  1.055  1.030
##  [4961]  1.068  1.055  1.013  1.045  0.942  1.042  1.063  0.970  1.056  1.042
##  [4971]  1.029  1.033  0.798  0.892  1.004  1.004  1.045  1.056  1.112  1.058
##  [4981]  1.090  1.098  1.124  1.117  0.827  0.934  1.089  1.096  1.107  1.084
##  [4991]  1.058  1.068  1.041  1.079  1.061  1.138  1.051  1.130  1.142  1.309
##  [5001]  0.969  1.321  1.844  1.522  0.827  1.031  1.068  1.282  0.810  0.796
##  [5011]  0.777  0.801  1.748  0.795  0.771  0.806  0.777  0.771  0.787  0.775
##  [5021]  0.795  0.801  0.775  0.777  0.775  0.773  0.791  0.799  1.064  0.761
##  [5031]  0.761  0.761  0.767  0.900  0.925  0.943  0.762  0.762  0.762  0.762
##  [5041]  0.762  0.762  0.762  0.766  0.766  0.763  0.761  0.761  0.761  0.763
##  [5051]  0.848  0.761  0.761  0.761  0.761  0.761  0.773  0.761  0.761  0.761
##  [5061]  0.761  0.761  0.761  0.827  0.761  0.761  0.797  0.793  0.812  0.807
##  [5071]  0.846  0.761  0.761  0.761  0.761  0.819  0.765  0.860  0.762  0.762
##  [5081]  0.765  0.762  0.766  0.765  0.776  0.770  0.851  0.796  1.031  1.309
##  [5091]  0.789  0.952  0.912  0.851  0.761  0.873  0.824  0.781  0.761  0.761
##  [5101]  0.761  0.761  0.761  0.761  0.761  0.849  0.811  0.791  0.788  0.825
##  [5111]  0.808  0.825  0.807  1.097  0.761  0.761  0.761  1.038  1.413  0.852
##  [5121]  0.892  0.837  0.762  0.829  0.762  0.763  0.783  0.772  0.784  0.804
##  [5131]  0.791  0.761  0.858  0.932  1.026  0.761  1.356  0.766  0.761  0.761
##  [5141]  0.761  0.761  0.761  0.761  0.761  0.761  0.865  0.828  0.801  0.824
##  [5151]  0.811  0.850  0.823  1.087  1.137  0.761  1.149  1.172  1.081  1.424
##  [5161]  0.964  0.784  0.770  0.774  0.771  0.770  0.819  0.765  0.773  0.773
##  [5171]  0.770  0.777  0.790  0.764  0.776  0.768  0.764  0.855  0.785  0.766
##  [5181]  0.765  0.767  0.802  0.792  0.780  0.801  0.788  0.772  0.774  0.762
##  [5191]  0.768  0.762  0.875  0.771  0.791  0.762  0.762  0.857  0.772  0.809
##  [5201]  0.781  0.799  0.781  0.762  0.769  0.778  0.776  0.771  0.766  0.763
##  [5211]  0.768  0.773  0.769  0.770  0.765  0.779  0.788  0.762  0.770  0.850
##  [5221]  0.962  0.763  0.762  0.762  0.762  0.904  0.765  0.767  0.763  0.761
##  [5231]  0.773  0.763  0.763  0.771  0.872  0.769  0.814  0.808  0.799  0.796
##  [5241]  0.902  0.810  0.794  0.786  0.803  0.788  0.789  0.811  0.816  0.813
##  [5251]  0.808  0.814  0.821  0.827  0.808  0.810  0.777  0.839  0.805  0.791
##  [5261]  0.797  0.790  0.769  0.762  0.762  0.762  0.829  0.801  0.772  0.806
##  [5271]  0.786  0.776  0.780  0.782  0.766  0.819  0.779  0.773  0.768  0.787
##  [5281]  0.837  1.310  0.868  0.786  0.784  0.762  0.765  0.762  0.762  0.794
##  [5291]  0.761  0.761  0.761  0.761  1.292  0.761  1.032  1.113  1.209  1.051
##  [5301]  0.761  0.761  0.865  0.761  0.761  0.761  0.761  0.761  0.761  0.791
##  [5311]  0.798  0.797  0.813  0.773  0.775  0.815  1.117  0.761  0.761  0.973
##  [5321]  1.116  0.979  0.837  0.761  0.765  0.793  0.773  0.769  0.783  0.782
##  [5331]  0.772  0.781  0.799  0.779  0.819  0.856  0.846  1.170  0.805  0.952
##  [5341]  0.873  1.444  0.820  0.785  0.805  0.797  0.807  0.822  0.817  0.838
##  [5351]  0.793  0.841  0.837  0.853  0.792  0.873  0.859  0.853  0.893  0.872
##  [5361]  0.875  0.847  0.823  0.840  0.877  0.902  2.365  2.365  0.819  0.793
##  [5371]  0.766  0.776  0.761  0.765  0.769  0.809  0.761  0.761  0.761  0.761
##  [5381]  0.762  0.766  1.064  0.978  1.035  0.981  1.072  0.956  1.010  0.979
##  [5391]  0.885  0.765  0.783  0.798  0.762  0.761  0.809  0.856  0.863  0.761
##  [5401]  0.910  0.761  0.761  0.761  0.761  0.761  0.761  0.761  1.824  0.864
##  [5411]  0.865  0.810  1.147  0.993  1.097  0.761  1.116  1.157  0.761  0.761
##  [5421]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
##  [5431]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
##  [5441]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  1.502  1.198
##  [5451]  0.763  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.766
##  [5461]  0.761  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.761  0.761
##  [5471]  0.761  0.761  0.761  0.821  0.836  0.789  0.761  0.823  0.761  0.761
##  [5481]  0.761  0.761  0.761  0.973  1.149  1.173  0.775  0.789  0.762  0.762
##  [5491]  0.837  0.762  1.154  1.690  0.848  0.828  0.828  0.761  0.761  0.761
##  [5501]  0.761  0.761  0.761  1.299  0.824  0.829  0.791  0.773  0.762  0.771
##  [5511]  0.762  0.762  0.762  0.762  0.761  0.761  0.802  0.773  1.337  0.761
##  [5521]  1.174  1.241  0.761  1.299  0.761  0.764  0.761  0.761  0.761  0.761
##  [5531]  0.761  0.761  0.919  0.975  0.871  0.875  1.079  0.936  1.098  0.851
##  [5541]  0.761  0.863  1.006  0.761  0.761  0.761  0.762  0.762  0.762  0.762
##  [5551]  0.762  0.762  0.762  0.762  0.762  0.761  2.038  0.764  0.764  0.762
##  [5561]  0.762  1.855  1.163  0.993  1.021  1.720  0.762  0.794  0.762  0.762
##  [5571]  0.762  0.762  0.803  0.803  1.244  0.761  1.166  0.761  0.761  0.762
##  [5581]  0.762  0.761  0.761  0.762  0.762  0.762  1.783  1.126  1.016  1.482
##  [5591]  1.638  1.998  1.679  1.699  1.722  1.628  1.096  1.700  1.762  2.446
##  [5601]  1.908  1.306  1.140  1.265  0.761  0.761  0.761  0.761  0.761  2.147
##  [5611]  1.410  1.222  1.176  1.856  0.762  0.762  0.762  0.762  0.762  0.762
##  [5621]  0.762  0.762  0.761  1.117  0.761  0.762  0.761  0.761  0.761  0.761
##  [5631]  0.761  1.260  0.920  1.009  1.144  1.121  1.276  1.084  1.128  1.155
##  [5641]  1.173  1.134  1.177  1.176  1.184  1.226  1.289  1.319  1.056  1.435
##  [5651]  1.130  1.011  1.000  1.065  3.222  1.734  1.109  1.031  0.770  1.812
##  [5661]  1.164  2.597  1.365  0.762  0.762  0.762  0.762  0.762  0.762  0.762
##  [5671]  1.132  1.133  1.283  0.761  0.761  0.772  0.791  0.818  0.761  0.771
##  [5681]  0.762  0.815  0.822  0.769  0.819  0.778  0.795  0.844  0.900  0.762
##  [5691]  1.113  1.174  1.039  1.192  1.199  1.074  0.788  0.792  0.981  0.911
##  [5701]  1.143  1.300  1.211  0.829  0.885  0.761  0.787  0.800  0.893  1.244
##  [5711]  2.174  1.353  1.169  1.025  1.259  1.098  1.031  1.159  1.128  1.349
##  [5721]  1.140  1.066  1.193  1.180  1.276  1.295  1.149  1.144  1.058  1.203
##  [5731]  1.011  1.215  1.272  1.287  1.244  1.354  1.327  1.272  3.344  1.294
##  [5741]  1.400  1.296  1.374  0.761  1.203  0.815  0.792  0.762  1.010  1.095
##  [5751]  1.080  1.158  1.207  0.771  0.836  1.002  0.833  0.977  0.762  0.797
##  [5761]  0.985  0.813  0.763  0.762  0.796  0.881  0.914  0.832  1.117  0.764
##  [5771]  1.333  0.761  1.072  0.761  0.764  0.762  0.955  0.995  1.184  1.133
##  [5781]  0.999  1.279  1.100  0.916  0.770  0.885  0.996  1.058  0.957  1.035
##  [5791]  1.135  0.774  0.768  0.771  0.762  0.762  0.768  0.878  0.887  0.761
##  [5801]  0.761  0.761  0.761  0.762  0.761  0.761  0.761  0.762  0.762  0.833
##  [5811]  0.761  0.878  0.762  0.762  0.761  0.770  0.761  0.762  0.769  0.761
##  [5821]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
##  [5831]  0.762  0.761  0.761  0.761  0.762  0.761  0.761  0.761  0.761  0.761
##  [5841]  0.835  0.761  0.761  0.761  0.762  1.123  1.256  1.408  0.761  0.765
##  [5851]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
##  [5861]  0.761  0.761  0.764  0.761  0.995  1.062  0.762  0.895  0.761  0.761
##  [5871]  0.761  0.871  0.761  1.631  1.184  1.121  1.075  1.267  3.512  0.761
##  [5881]  0.761  0.761  0.761  0.761  0.761  0.804  0.761  0.761  0.814  0.813
##  [5891]  0.761  0.761  0.979  0.761  0.761  1.631  0.761  0.897  0.762  0.761
##  [5901]  2.297  1.059  0.762  1.158  0.761  0.761  1.074  1.253  1.264  1.245
##  [5911]  1.204  0.832  1.157  1.120  1.103  0.987  0.906  1.059  1.066  0.884
##  [5921]  0.999  1.054  0.783  0.786  0.782  1.187  1.268  0.786  0.766  1.231
##  [5931]  0.762  0.768  0.761  0.761  0.761  1.292  1.327  1.145  1.052  1.152
##  [5941]  1.124  1.108  1.051  1.598  1.568  1.727  1.552  1.684  0.761  0.761
##  [5951]  1.596  1.775  0.761  1.452  1.707  1.493  1.488  1.192  0.761  0.761
##  [5961]  0.762  0.762  0.761  0.761  0.762  0.762  1.220  1.941  1.927  0.761
##  [5971]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.762  0.761  0.761
##  [5981]  0.761  0.761  0.761  0.761  2.037  1.246  1.844  0.761  0.762  0.762
##  [5991]  0.761  0.761  0.761  0.762  0.762  0.762  0.762  0.762  0.762  0.762
##  [6001]  0.762  0.762  0.761  1.952  0.761  0.762  1.549  2.321  0.761  0.761
##  [6011]  0.761  0.761  0.762  0.762  0.761  0.761  0.761  0.762  0.762  0.762
##  [6021]  0.762  0.762  0.762  0.762  0.762  0.762  0.829  1.580  1.279  1.138
##  [6031]  1.264  1.212  2.022  1.286  0.761  0.761  0.761  0.761  0.761  0.761
##  [6041]  0.766  0.761  0.763  0.761  0.761  1.311  1.303  1.136  0.761  0.761
##  [6051]  0.761  0.761  0.761  1.769  1.898  2.127  2.362  1.746  0.761  0.761
##  [6061]  0.761  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  1.080
##  [6071]  0.761  1.912  0.761  1.374  0.761  0.762  0.998  1.139  1.195  1.115
##  [6081]  1.054  1.153  1.185  1.287  1.313  0.992  1.091  0.892  0.986  1.160
##  [6091]  1.321  1.109  1.398  1.187  0.787  0.813  1.029  1.066  0.920  1.114
##  [6101]  1.127  1.381  1.267  0.761  0.792  0.761  0.761  0.799  0.761  0.761
##  [6111]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
##  [6121]  0.761  0.761  0.761  0.761  0.761  0.761  0.762  0.761  1.514  1.309
##  [6131]  0.906  0.761  0.761  0.762  0.761  0.761  0.761  0.762  0.762  1.262
##  [6141]  0.939  1.333  0.987  0.815  0.778  0.804  1.386  1.188  1.038  0.761
##  [6151]  0.761  0.761  1.159  0.761  0.806  0.962  1.751  1.486  1.412  0.761
##  [6161]  1.323  0.761  0.761  0.761  0.761  0.761  0.834  0.940  1.208  1.284
##  [6171]  1.158  1.189  1.121  0.980  1.047  1.331  1.224  1.094  1.146  0.765
##  [6181]  1.138  0.972  1.326  1.038  1.355  0.995  0.763  0.807  0.844  0.913
##  [6191]  0.858  1.043  0.854  1.397  0.901  0.834  0.950  1.015  1.082  1.011
##  [6201]  1.060  1.046  1.085  1.069  1.113  1.121  1.094  1.081  1.157  1.139
##  [6211]  1.154  1.183  1.223  1.234  1.309  1.520  1.224  1.183  1.188  1.161
##  [6221]  0.995  1.217  1.133  0.761  1.848  0.761  0.761  0.761  0.762  0.762
##  [6231]  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.761  1.272  0.761
##  [6241]  1.029  1.755  0.761  0.762  0.762  0.762  0.762  0.762  0.762  0.762
##  [6251]  0.762  0.762  1.132  0.762  0.761  0.762  1.311  0.947  2.638  0.988
##  [6261]  1.070  1.137  1.105  1.219  1.216  1.253  1.216  1.185  1.213  1.204
##  [6271]  1.174  1.306  1.985  1.381  1.128  1.069  1.393  1.078  1.155  1.163
##  [6281]  1.211  1.290  1.075  1.181  1.015  0.764  1.167  0.886  0.887  0.762
##  [6291]  0.993  1.118  0.981  1.197  1.138  3.118  0.764  0.761  0.761  2.782
##  [6301]  1.025  1.056  0.796  0.776  0.802  0.764  0.932  0.799  0.762  0.838
##  [6311]  0.883  0.777  0.762  0.957  1.108  0.806  0.762  0.762  0.762  0.771
##  [6321]  0.761  0.762  0.762  0.762  0.862  0.761  1.288  0.764  0.761  0.761
##  [6331]  0.761  0.761  1.008  0.920  1.210  0.851  0.857  0.909  1.065  1.033
##  [6341]  0.955  0.882  0.988  1.050  0.820  0.964  0.785  1.280  0.764  1.042
##  [6351]  0.761  1.113  0.928  0.801  0.881  0.974  1.001  0.761  0.761  0.761
##  [6361]  0.762  0.762  0.981  1.206  1.098  1.144  0.955  0.947  0.841  0.820
##  [6371]  2.620  1.826  2.352  1.249  1.329  1.486  0.806  0.774  1.133  1.003
##  [6381]  1.054  1.309  1.126  1.153  1.066  1.104  1.246  1.070  1.134  0.761
##  [6391]  0.767  0.775  1.952  0.900  1.174  1.090  0.833  0.784  0.888  0.858
##  [6401]  0.862  1.002  1.044  1.111  1.149  1.074  1.033  1.024  1.174  1.329
##  [6411]  1.107  0.804  1.001  1.372  1.191  1.369  1.181  1.150  1.179  1.188
##  [6421]  1.082  1.179  1.027  0.999  1.214  1.217  1.348  2.056  0.769  1.511
##  [6431]  0.761  0.762  0.761  0.761  0.761  0.761  1.618  0.961  0.906  1.163
##  [6441]  1.148  1.183  1.302  1.029  1.174  1.209  1.316  1.224  1.196  1.118
##  [6451]  1.208  1.281  0.911  0.968  1.353  0.762  0.899  1.001  1.003  1.044
##  [6461]  0.935  0.899  0.944  0.802  2.113  1.383  1.134  1.131  1.217  1.227
##  [6471]  1.155  1.212  1.011  1.178  1.196  0.761  1.213  1.215  1.237  1.130
##  [6481]  0.824  0.963  1.156  1.012  0.800  0.811  1.129  1.245  0.877  0.958
##  [6491]  1.008  1.130  3.690  0.933  0.765  1.435  1.226  2.928  1.320  0.813
##  [6501]  0.852  0.899  0.761  0.762  0.814  0.805  0.827  0.798  0.955  0.837
##  [6511]  1.860  1.398  0.937  0.874  1.348  0.982  0.761  0.769  0.786  0.761
##  [6521]  0.761  0.761  0.761  0.762  0.760  0.901  0.762  0.761  0.761  0.761
##  [6531]  0.761  0.761  0.761  0.761  0.853  0.943  0.947  1.190  1.863  3.208
##  [6541]  1.336  0.920  1.005  3.019  0.761  0.788  0.766  0.943  1.014  0.761
##  [6551]  0.762  0.762  0.760  1.100  0.761  1.164  1.103  1.140  1.080  1.036
##  [6561]  0.818  0.762  0.780  0.809  0.789  0.818  0.965  0.762  2.057  1.061
##  [6571]  0.892  1.023  1.267  0.992  0.957  0.966  0.892  0.966  1.178  1.036
##  [6581]  1.273  1.156  1.029  1.007  1.225  1.943  1.217  0.762  0.762  0.762
##  [6591]  0.762  0.762  0.762  0.761  0.762  0.761  0.762  0.762  0.762  0.762
##  [6601]  0.762  1.221  0.761  1.404  0.761  0.762  0.762  0.762  0.762  0.851
##  [6611]  1.024  0.762  1.211  0.782  0.762  1.767  1.202  1.076  1.201  1.061
##  [6621]  0.761  1.113  3.092  1.218  1.142  1.483  0.761  0.761  0.761  0.761
##  [6631]  0.761  0.761  0.761  0.943  0.761  1.759  0.866  1.112  0.761  0.816
##  [6641]  0.761  0.761  0.761  0.761  0.761  0.762  0.762  0.762  0.762  0.762
##  [6651]  0.762  0.762  0.762  0.764  0.762  1.478  0.762  0.761  0.762  0.762
##  [6661]  0.762  0.762  0.762  0.762  0.761  0.764  2.367  0.761  0.873  0.762
##  [6671]  1.313  1.866  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762
##  [6681]  0.764  0.762  0.760  0.762  0.761  0.764  0.787  0.796  0.761  0.864
##  [6691]  1.248  1.009  1.163  0.831  1.703  1.238  0.761  0.762  0.962  1.368
##  [6701]  1.177  1.950  1.052  0.762  0.924  0.932  0.846  0.762  0.897  0.762
##  [6711]  0.905  0.761  0.761  0.762  0.762  0.995  0.761  0.761  0.761  0.761
##  [6721]  0.761  0.902  0.761  0.761  0.761  0.777  0.761  1.447  1.470  0.761
##  [6731]  0.761  0.761  1.857  0.762  1.585  0.775  0.761  0.761  0.765  0.762
##  [6741]  0.762  0.761  0.763  0.765  0.762  0.762  0.762  0.762  0.765  0.765
##  [6751]  0.762  0.762  0.762  0.765  0.765  0.762  0.762  0.761  0.766  0.765
##  [6761]  0.762  0.762  0.761  0.765  0.765  0.762  0.762  0.762  0.765  0.761
##  [6771]  0.762  0.762  0.761  0.765  0.765  0.762  0.762  0.762  0.761  0.765
##  [6781]  0.765  0.762  0.762  0.762  0.766  0.785  0.825  0.762  0.762  0.761
##  [6791]  0.765  0.791  0.762  0.762  0.761  0.766  0.870  0.762  0.762  0.761
##  [6801]  0.765  0.765  0.762  0.762  0.761  0.765  0.765  0.762  0.765  0.808
##  [6811]  0.765  0.765  0.762  0.935  0.761  0.765  0.761  0.762  0.762  0.761
##  [6821]  0.765  0.765  0.762  0.762  0.761  0.765  0.765  0.762  0.761  0.761
##  [6831]  0.765  0.765  0.762  0.762  0.761  0.765  0.762  0.762  0.761  0.765
##  [6841]  0.762  0.761  0.761  0.765  0.762  0.762  0.761  0.765  0.762  0.761
##  [6851]  0.761  0.765  0.762  0.762  0.761  0.765  0.762  0.762  0.761  0.765
##  [6861]  0.762  0.762  0.761  0.765  0.762  0.761  0.766  0.765  0.762  0.762
##  [6871]  0.765  0.761  0.765  0.762  0.762  0.765  0.762  0.765  0.765  0.762
##  [6881]  0.765  0.765  0.762  0.765  0.765  0.762  0.761  0.765  0.761  0.765
##  [6891]  0.762  0.762  0.765  0.761  0.787  0.762  0.786  0.765  0.761  0.765
##  [6901]  0.762  0.762  0.765  0.761  0.765  0.762  0.761  0.765  0.761  0.765
##  [6911]  0.762  0.762  0.762  0.761  0.761  0.765  0.762  0.762  0.762  0.761
##  [6921]  0.761  0.765  0.762  0.762  0.762  0.761  0.761  0.765  0.762  0.762
##  [6931]  0.765  0.761  0.761  0.765  0.762  0.762  0.761  0.761  0.761  0.765
##  [6941]  0.762  0.762  0.762  0.761  0.761  0.765  0.762  0.762  0.762  0.761
##  [6951]  0.761  0.761  0.762  0.762  0.762  0.761  0.761  0.765  0.762  0.762
##  [6961]  0.762  0.761  0.761  0.765  0.762  0.762  0.762  0.761  0.762  0.765
##  [6971]  0.762  0.762  0.762  0.761  0.761  0.763  0.762  0.762  0.761  0.761
##  [6981]  0.761  0.765  0.762  0.762  0.762  0.761  0.761  0.765  0.762  0.762
##  [6991]  0.762  0.761  0.762  0.761  0.762  0.762  0.765  0.761  0.767  0.765
##  [7001]  0.762  0.762  0.763  0.766  0.765  0.762  0.762  0.764  0.765  0.765
##  [7011]  0.762  0.762  0.764  0.765  0.765  0.762  0.762  0.761  0.767  0.765
##  [7021]  0.762  0.762  0.763  0.765  0.765  0.762  0.762  0.764  0.767  0.761
##  [7031]  0.762  0.762  0.762  0.765  0.765  0.762  0.762  0.761  0.766  0.764
##  [7041]  0.762  0.762  0.765  0.763  0.763  0.762  0.762  0.762  0.765  0.762
##  [7051]  0.762  0.762  0.763  0.766  0.762  0.762  0.762  0.761  0.765  0.765
##  [7061]  0.762  0.762  0.761  0.765  0.765  0.762  0.761  0.761  0.766  0.765
##  [7071]  0.762  0.761  0.761  0.766  0.762  0.762  0.762  0.761  0.765  0.765
##  [7081]  0.762  0.762  0.761  0.765  0.765  0.762  0.761  0.761  0.765  0.765
##  [7091]  0.762  0.762  0.761  0.765  0.762  0.762  0.761  0.765  0.762  0.761
##  [7101]  0.761  0.765  0.762  0.762  0.761  0.765  0.762  0.761  0.761  0.945
##  [7111]  0.762  0.762  0.761  0.892  1.161  0.762  0.762  0.880  1.158  1.270
##  [7121]  1.169  0.833  0.821  1.011  1.321  0.827  0.854  1.288  0.995  0.956
##  [7131]  1.261  0.946  1.296  0.899  1.205  0.955  1.175  0.832  1.298  0.839
##  [7141]  1.316  0.873  1.197  0.853  0.767  1.146  0.825  0.762  0.765  1.226
##  [7151]  0.765  0.762  0.815  0.768  0.767  0.765  0.762  0.765  0.762  0.762
##  [7161]  0.761  0.765  0.761  0.765  0.762  0.765  0.761  0.765  0.761  0.765
##  [7171]  0.762  0.765  0.762  0.762  0.761  0.761  0.765  0.762  0.762  0.761
##  [7181]  0.761  0.765  0.762  0.762  0.761  0.762  0.765  0.762  0.761  0.761
##  [7191]  0.762  0.765  0.762  0.761  0.761  0.762  0.765  0.762  0.762  0.761
##  [7201]  0.763  0.765  0.762  0.762  0.761  0.762  0.761  0.762  0.762  0.761
##  [7211]  0.763  0.765  0.762  0.762  0.761  0.763  0.765  0.762  0.762  0.763
##  [7221]  0.764  0.765  0.762  0.762  0.765  0.762  0.764  0.762  0.761  0.762
##  [7231]  0.765  0.765  0.762  0.762  0.763  0.764  0.765  0.762  0.762  0.762
##  [7241]  0.804  0.761  0.762  0.768  0.761  0.762  0.765  0.762  0.762  0.762
##  [7251]  0.761  0.764  0.765  0.762  0.762  0.762  0.762  0.765  0.765  0.762
##  [7261]  0.762  0.762  0.761  0.765  0.765  0.762  0.762  0.762  0.761  0.765
##  [7271]  0.765  0.762  0.762  0.762  0.761  0.765  0.765  0.762  0.762  0.762
##  [7281]  0.761  0.765  0.761  0.762  0.762  0.762  0.761  0.765  0.765  0.762
##  [7291]  0.762  0.762  0.761  0.765  0.765  0.762  0.762  0.762  0.766  0.762
##  [7301]  0.762  0.762  0.762  0.762  0.761  0.765  0.762  0.762  0.762  0.762
##  [7311]  0.762  0.765  0.762  0.762  0.762  0.762  0.761  0.765  0.765  0.762
##  [7321]  0.762  0.805  0.761  0.765  0.765  0.762  0.762  0.761  0.761  0.765
##  [7331]  0.765  0.762  0.762  0.784  0.761  0.765  0.761  0.762  0.762  0.762
##  [7341]  0.761  0.765  0.765  0.762  0.762  0.762  0.761  0.765  0.765  0.762
##  [7351]  0.762  0.761  0.783  0.765  0.765  0.762  0.762  0.762  0.761  0.765
##  [7361]  0.762  0.762  0.762  0.761  0.765  0.762  0.762  0.761  0.761  0.765
##  [7371]  0.762  0.762  0.780  0.761  0.765  0.762  0.761  0.761  0.765  0.762
##  [7381]  0.762  0.761  0.765  0.762  0.762  0.761  0.765  0.762  0.762  0.761
##  [7391]  0.765  0.762  0.761  0.781  0.765  0.762  0.762  0.765  0.761  0.765
##  [7401]  0.762  0.762  0.765  0.762  0.765  0.761  0.765  0.762  0.765  0.765
##  [7411]  0.762  0.765  0.765  0.762  0.761  0.765  0.761  0.765  0.762  0.762
##  [7421]  0.765  0.762  0.762  0.762  0.761  0.765  0.761  0.765  0.762  0.762
##  [7431]  0.765  0.761  0.765  0.762  0.761  0.765  0.761  0.765  0.762  0.762
##  [7441]  0.762  0.761  0.761  0.765  0.762  0.762  0.762  0.761  0.762  0.765
##  [7451]  0.762  0.762  0.762  0.761  0.761  0.765  0.762  0.762  0.761  0.761
##  [7461]  0.761  0.765  0.762  0.762  0.761  0.761  0.761  0.765  0.762  0.762
##  [7471]  0.762  0.761  0.761  0.765  0.762  0.762  0.762  0.761  0.762  0.761
##  [7481]  0.762  0.762  0.762  0.761  0.761  0.765  0.762  0.762  0.762  0.761
##  [7491]  0.761  0.765  0.762  0.762  0.762  0.761  0.761  0.765  0.762  0.762
##  [7501]  0.762  0.769  0.762  0.763  0.762  0.762  0.762  0.761  0.762  0.765
##  [7511]  0.762  0.762  0.762  0.761  0.762  0.765  0.762  0.762  0.762  0.761
##  [7521]  0.773  0.761  0.762  0.762  0.764  0.762  0.943  0.763  0.763  0.795
##  [7531]  0.762  0.970  0.763  0.788  0.950  0.907  0.913  0.763  0.763  0.827
##  [7541]  0.764  0.773  0.763  0.763  0.763  0.826  0.765  0.763  0.763  1.340
##  [7551]  0.855  0.797  0.763  0.952  0.872  0.815  0.862  0.763  0.763  0.832
##  [7561]  0.874  0.841  0.763  0.763  0.787  0.906  0.810  0.763  0.763  0.848
##  [7571]  0.762  0.849  0.763  0.763  0.981  0.824  0.762  0.763  0.763  0.763
##  [7581]  0.843  0.764  0.763  0.865  0.763  0.803  0.764  0.763  0.763  0.845
##  [7591]  0.772  0.764  0.763  0.763  0.761  0.764  0.785  0.763  0.763  0.991
##  [7601]  0.845  0.801  0.763  0.763  0.792  0.901  0.764  0.763  0.763  0.802
##  [7611]  0.762  0.819  0.764  0.763  0.763  0.761  0.762  0.842  0.770  0.763
##  [7621]  0.966  0.811  0.826  0.761  0.763  0.763  0.763  0.765  0.763  0.763
##  [7631]  0.762  0.912  0.763  0.763  0.762  0.912  0.763  0.762  1.035  0.763
##  [7641]  0.885  0.762  1.036  0.761  0.763  0.762  1.081  0.762  1.038  1.457
##  [7651]  0.857  0.762  0.973  0.762  1.036  0.946  0.766  0.762  1.072  1.054
##  [7661]  1.059  0.909  0.763  0.762  1.059  0.761  1.018  0.890  0.762  1.058
##  [7671]  0.761  1.044  0.815  0.924  1.006  0.761  1.034  0.828  0.870  1.059
##  [7681]  0.761  0.970  0.886  0.987  1.019  0.761  1.002  0.917  0.851  0.910
##  [7691]  0.761  0.881  0.869  1.047  0.764  0.762  0.920  0.808  0.806  0.764
##  [7701]  0.762  0.762  0.788  0.763  0.764  0.853  0.762  0.765  0.764  0.762
##  [7711]  0.825  0.760  0.763  0.764  0.762  0.832  0.763  0.766  0.995  0.762
##  [7721]  0.764  0.763  0.763  1.048  0.762  0.768  0.763  0.763  0.932  0.762
##  [7731]  0.827  0.763  0.763  0.929  0.762  0.791  0.763  0.763  0.930  0.762
##  [7741]  0.799  0.763  0.763  0.763  0.762  0.790  0.763  0.763  0.765  0.762
##  [7751]  0.761  0.763  0.763  0.763  0.762  0.764  0.763  0.763  0.788  0.761
##  [7761]  0.764  0.763  0.763  0.896  0.762  0.764  0.763  0.763  0.858  0.762
##  [7771]  0.848  0.763  0.873  0.902  0.762  1.036  0.763  0.783  0.843  0.762
##  [7781]  0.909  0.763  0.763  0.872  0.762  0.906  0.763  0.763  0.762  0.763
##  [7791]  0.762  1.034  1.049  1.046  0.858  0.762  1.044  1.049  1.023  0.876
##  [7801]  0.762  1.072  1.053  1.042  1.038  1.032  0.896  1.044  0.983  0.976
##  [7811]  1.084  0.762  0.951  1.052  0.987  1.015  1.046  0.993  1.061  0.987
##  [7821]  1.031  1.077  0.762  1.013  1.037  1.052  1.018  1.056  0.855  1.059
##  [7831]  1.036  1.024  1.073  0.762  0.764  1.039  1.027  1.002  1.044  0.997
##  [7841]  0.804  1.023  0.989  0.993  1.087  0.762  0.986  1.046  1.002  1.057
##  [7851]  1.087  0.766  1.040  1.051  1.004  0.925  0.764  0.898  1.042  1.046
##  [7861]  0.874  0.775  1.022  1.009  1.046  0.879  0.884  1.005  1.002  1.042
##  [7871]  0.876  0.764  0.969  0.954  0.956  0.864  0.816  0.998  0.893  0.953
##  [7881]  0.833  0.818  1.061  0.984  1.029  0.908  0.767  1.042  1.054  1.050
##  [7891]  0.911  0.765  0.761  1.021  1.020  0.907  0.762  0.782  0.761  1.042
##  [7901]  1.050  0.853  0.768  0.761  1.039  1.090  0.839  0.764  0.761  1.011
##  [7911]  0.762  0.989  0.762  0.799  0.761  1.006  0.762  0.910  0.762  0.943
##  [7921]  5.591  1.031  0.762  1.005  0.762  0.944  1.637  1.004  0.762  0.942
##  [7931]  0.762  0.971  0.976  1.015  0.762  1.080  0.761  0.997  0.976  0.975
##  [7941]  0.762  1.018  0.761  0.985  1.007  0.762  1.015  0.761  1.014  1.014
##  [7951]  0.976  0.913  0.761  0.999  1.003  1.036  1.028  0.761  1.049  1.030
##  [7961]  0.998  0.945  0.761  1.025  1.023  1.025  1.057  0.761  1.040  1.050
##  [7971]  0.993  0.935  0.762  0.806  1.031  1.013  0.908  0.920  1.040  1.008
##  [7981]  0.982  0.950  0.762  0.975  0.923  0.762  0.970  1.050  1.014  0.770
##  [7991]  0.960  0.762  0.906  1.053  1.006  1.059  0.762  1.009  1.052  1.006
##  [8001]  1.085  0.762  1.001  1.039  0.984  1.080  0.762  0.762  0.917  1.046
##  [8011]  1.032  1.073  0.762  0.762  1.051  1.044  1.001  1.061  0.762  0.762
##  [8021]  1.043  1.050  1.005  1.047  0.762  1.061  1.032  0.910  1.079  0.762
##  [8031]  0.762  0.822  1.009  1.009  1.049  0.762  0.762  0.938  1.055  0.995
##  [8041]  1.048  0.761  0.987  1.030  1.046  0.969  0.762  0.858  1.028  1.045
##  [8051]  1.044  0.762  0.762  0.851  1.040  1.057  1.082  0.762  0.762  1.023
##  [8061]  1.029  1.021  1.051  0.762  1.039  1.033  1.021  1.016  0.762  1.018
##  [8071]  1.032  1.007  0.762  0.818  0.818  0.792  0.762  0.832  0.846  0.762
##  [8081]  0.811  0.824  0.762  0.761  0.817  0.762  0.817  0.816  0.762  0.828
##  [8091]  0.818  0.762  0.846  0.802  0.762  0.834  0.817  0.762  0.828  0.827
##  [8101]  0.828  0.819  0.762  0.807  0.823  0.762  0.813  0.817  0.762  0.838
##  [8111]  0.831  0.761  0.849  0.813  0.761  0.847  0.821  0.762  0.835  0.830
##  [8121]  0.762  0.822  0.818  0.761  0.830  0.831  0.762  0.828  0.816  0.762
##  [8131]  0.822  0.823  0.761  0.830  0.815  0.762  0.800  0.810  0.761  0.830
##  [8141]  0.818  0.762  0.817  0.806  0.762  0.809  0.812  0.762  0.807  0.805
##  [8151]  0.761  0.826  0.828  0.762  0.761  0.802  0.761  0.761  0.821  0.762
##  [8161]  0.761  0.818  0.864  0.761  0.845  0.761  0.828  0.761  0.781  0.815
##  [8171]  0.856  0.803  0.785  0.801  0.824  0.800  0.807  0.811  0.804  0.798
##  [8181]  0.812  0.805  0.796  0.813  0.806  0.830  0.812  0.821  0.803  0.805
##  [8191]  0.767  0.835  0.834  0.810  0.847  0.799  0.803  0.797  0.804  0.802
##  [8201]  0.782  0.805  0.815  0.817  0.848  0.817  0.809  0.761  0.762  0.761
##  [8211]  0.762  0.762  0.762  0.762  0.762  0.761  0.762  0.761  0.762  0.761
##  [8221]  0.762  0.761  0.762  0.761  0.762  0.766  0.762  0.761  0.762  0.763
##  [8231]  0.762  0.761  0.762  0.761  0.761  0.761  0.761  0.761  0.762  0.761
##  [8241]  0.762  0.761  0.761  0.761  0.762  0.761  0.762  0.761  0.761  0.761
##  [8251]  0.762  0.763  0.761  0.761  0.762  0.761  0.762  0.761  0.762  0.761
##  [8261]  0.761  0.798  0.762  0.761  0.761  0.927  0.762  0.765  0.766  0.765
##  [8271]  0.761  0.767  0.762  0.765  0.761  0.766  0.762  0.765  0.761  0.765
##  [8281]  0.762  0.761  0.762  0.761  0.762  0.761  0.761  0.761  0.761  0.761
##  [8291]  0.762  0.761  0.762  0.761  0.762  0.761  0.762  0.761  0.762  0.762
##  [8301]  0.762  0.761  0.761  0.765  0.762  0.761  0.762  0.761  0.761  0.784
##  [8311]  0.783  0.762  0.762  0.791  0.815  0.828  0.762  0.762  0.776  0.795
##  [8321]  0.814  0.762  0.762  0.830  0.822  0.773  0.762  0.762  0.761  0.845
##  [8331]  0.798  0.762  0.762  0.776  0.846  0.773  0.762  0.762  0.764  0.834
##  [8341]  0.761  0.762  0.762  0.804  0.815  0.852  0.762  0.762  0.772  0.824
##  [8351]  0.858  0.762  0.762  0.812  0.822  0.839  0.762  0.762  0.815  0.835
##  [8361]  0.847  0.762  0.762  0.837  0.858  0.797  0.762  0.762  0.825  0.842
##  [8371]  0.859  0.762  0.762  0.845  0.843  0.860  0.762  0.761  0.859  0.780
##  [8381]  0.843  0.762  0.761  0.852  0.823  0.838  0.762  0.854  0.847  0.856
##  [8391]  0.762  0.815  0.849  0.840  0.761  0.822  0.827  0.830  0.762  0.826
##  [8401]  0.812  0.761  0.762  0.827  0.826  0.761  0.761  0.818  0.846  0.761
##  [8411]  0.762  0.812  0.851  0.761  0.761  0.841  1.072  0.761  0.762  0.825
##  [8421]  0.831  0.766  0.762  0.838  1.117  1.119  0.762  1.316  1.195  1.073
##  [8431]  1.233  1.367  1.512  1.272  1.201  1.280  1.487  1.206  1.136  1.311
##  [8441]  1.310  1.081  1.224  1.307  1.186  1.325  1.330  1.192  1.296  1.321
##  [8451]  1.230  0.859  1.289  0.918  0.761  0.851  0.761  0.835  0.855  0.847
##  [8461]  0.762  0.840  0.825  0.850  0.761  0.850  0.826  0.842  0.762  0.841
##  [8471]  0.830  0.851  0.761  0.856  0.826  0.861  0.762  0.762  0.853  0.836
##  [8481]  0.827  0.762  0.762  0.857  0.812  0.840  0.762  0.762  0.854  0.821
##  [8491]  0.863  0.762  0.761  0.845  0.835  0.863  0.762  0.761  0.851  0.832
##  [8501]  0.856  0.762  0.762  0.843  0.839  0.861  0.762  0.762  0.847  0.841
##  [8511]  0.763  0.762  0.762  0.856  0.850  0.772  0.762  0.762  0.830  0.837
##  [8521]  0.816  0.762  0.762  0.822  0.808  0.808  0.762  0.762  0.839  0.808
##  [8531]  0.849  0.762  0.761  0.823  0.835  0.851  0.762  0.762  0.800  0.785
##  [8541]  0.819  0.762  0.762  0.785  0.820  0.765  0.762  0.783  0.761  0.762
##  [8551]  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762
##  [8561]  0.762  0.762  0.761  0.761  0.762  0.762  1.706  0.761  0.762  0.761
##  [8571]  0.762  0.761  0.762  0.762  0.762  0.761  0.761  0.762  0.762  0.762
##  [8581]  0.762  0.762  0.762  0.761  0.762  0.762  0.761  0.761  0.761  0.761
##  [8591]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
##  [8601]  0.761  0.761  0.762  0.761  0.762  0.761  0.761  0.761  0.761  0.761
##  [8611]  0.761  0.761  0.761  0.761  0.761  0.761  0.762  0.761  0.761  0.761
##  [8621]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.762  0.761
##  [8631]  0.761  0.761  0.761  0.761  0.761  0.762  0.761  0.761  0.761  0.761
##  [8641]  0.762  0.761  0.761  0.761  0.762  0.761  0.761  0.761  0.762  0.761
##  [8651]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
##  [8661]  0.762  0.761  0.761  0.761  0.762  0.761  0.761  0.761  0.762  0.761
##  [8671]  0.761  0.761  0.761  0.762  0.761  0.761  0.761  0.761  0.762  0.761
##  [8681]  0.761  0.761  0.762  0.761  0.761  0.761  0.761  0.761  0.761  0.761
##  [8691]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
##  [8701]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
##  [8711]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.762  0.761
##  [8721]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
##  [8731]  0.762  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
##  [8741]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
##  [8751]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
##  [8761]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.762  0.761  0.761
##  [8771]  0.761  0.761  0.761  0.761  0.761  0.762  0.761  0.761  1.579  1.875
##  [8781]  0.761  1.100  0.761  0.761  0.761  0.761  0.761  0.761  0.762  0.761
##  [8791]  0.761  0.805  0.795  0.787  0.774  0.765  0.769  0.772  0.768  0.777
##  [8801]  0.775  0.767  0.775  0.770  0.772  0.776  0.784  0.776  0.784  0.776
##  [8811]  0.784  0.768  0.770  0.775  0.775  0.770  0.768  0.773  0.771  0.771
##  [8821]  0.772  0.771  0.781  0.773  0.768  0.766  0.778  0.771  0.803  0.771
##  [8831]  0.781  0.778  0.771  0.770  0.771  0.780  0.775  0.780  0.772  0.780
##  [8841]  0.771  0.768  0.770  0.775  0.781  0.785  0.775  0.771  0.767  0.773
##  [8851]  0.771  0.770  0.774  0.773  0.772  0.769  0.770  0.766  0.768  0.775
##  [8861]  0.770  0.782  0.768  0.768  0.772  0.769  0.779  0.772  0.775  0.781
##  [8871]  0.778  0.787  0.784  0.779  0.771  0.779  0.791  0.786  0.773  0.777
##  [8881]  0.781  0.782  0.784  0.783  0.773  0.775  0.783  0.782  0.773  0.778
##  [8891]  0.774  0.768  0.771  0.768  0.780  0.776  0.782  0.794  0.785  0.781
##  [8901]  0.783  0.782  0.792  0.788  0.786  0.787  0.776  0.774  0.764  0.766
##  [8911]  0.766  0.763  0.766  0.768  0.768  0.767  0.766  0.769  0.770  0.770
##  [8921]  0.771  0.766  0.764  0.779  0.831  0.819  0.768  0.779  0.775  0.769
##  [8931]  0.770  0.767  0.767  0.770  0.767  0.767  0.769  0.769  0.769  0.765
##  [8941]  0.766  0.774  0.772  0.776  0.782  0.773  0.769  0.772  0.770  0.770
##  [8951]  0.771  0.771  0.769  0.770  0.767  0.768  0.817  0.778  0.777  0.768
##  [8961]  0.779  0.778  0.775  0.770  0.780  0.779  0.785  0.779  0.770  0.777
##  [8971]  0.772  0.776  0.783  0.781  0.775  0.774  0.776  0.771  0.785  0.797
##  [8981]  0.795  0.775  0.772  0.774  0.775  0.783  0.774  0.776  0.785  0.785
##  [8991]  0.786  0.785  0.778  0.777  0.773  0.779  0.774  0.770  0.778  0.780
##  [9001]  0.770  0.776  0.774  0.772  0.773  0.771  0.770  0.768  0.773  0.770
##  [9011]  0.770  0.773  0.774  0.765  0.769  0.766  0.766  0.777  0.777  0.773
##  [9021]  0.768  0.772  0.769  0.778  0.776  0.778  0.773  0.777  0.773  0.771
##  [9031]  0.772  0.765  0.765  0.765  0.764  0.764  0.763  0.764  0.765  0.763
##  [9041]  0.765  0.763  0.782  1.418  0.763  0.763  0.764  0.765  0.763  0.763
##  [9051]  0.764  0.763  0.766  0.763  0.764  0.766  0.765  0.769  0.764  0.765
##  [9061]  0.765  0.764  0.765  0.767  0.765  0.767  0.764  0.765  0.762  0.765
##  [9071]  0.764  0.764  0.764  0.764  0.765  0.767  0.766  0.768  0.767  0.764
##  [9081]  0.766  0.764  0.765  0.764  0.767  0.767  0.765  0.763  0.764  0.769
##  [9091]  0.770  0.766  0.763  0.767  0.765  0.764  0.763  0.764  0.768  0.765
##  [9101]  0.766  0.766  0.767  0.768  0.763  0.764  0.765  0.766  0.763  0.763
##  [9111]  0.763  0.763  0.763  0.764  0.765  0.764  0.763  0.765  0.768  0.765
##  [9121]  0.763  0.764  0.764  0.765  0.763  0.764  0.783  0.771  0.792  0.763
##  [9131]  0.773  0.778  0.776  0.767  0.767  0.766  0.766  0.764  0.770  0.768
##  [9141]  0.779  0.808  0.821  0.799  0.776  0.801  0.818  0.808  0.815  0.811
##  [9151]  0.773  0.770  0.766  0.767  0.773  0.768  0.776  0.774  0.772  0.773
##  [9161]  0.773  0.765  0.762  0.763  0.768  0.767  0.763  0.762  0.764  0.763
##  [9171]  0.763  0.767  0.765  0.764  0.763  0.766  0.763  0.765  0.763  0.764
##  [9181]  0.764  0.763  0.768  0.765  0.765  0.765  0.766  0.763  0.763  0.764
##  [9191]  0.765  0.763  0.765  0.765  0.763  0.772  0.764  0.764  0.764  0.763
##  [9201]  0.763  0.763  0.765  0.764  0.764  0.765  0.764  0.763  0.763  0.762
##  [9211]  0.762  0.762  0.762  0.762  0.763  0.762  0.763  0.764  0.763  0.763
##  [9221]  0.763  0.762  0.763  0.763  0.764  0.764  0.763  0.763  0.770  0.776
##  [9231]  0.764  0.762  0.784  0.801  0.782  0.786  0.803  0.780  0.818  0.836
##  [9241]  0.808  0.787  0.798  0.833  0.803  0.779  0.786  0.783  0.763  0.781
##  [9251]  0.763  0.779  0.772  0.772  0.784  0.781  0.771  0.774  0.794  0.790
##  [9261]  0.774  0.770  0.790  0.767  0.765  0.764  0.814  0.766  0.790  0.762
##  [9271]  0.787  0.764  0.779  0.795  0.771  0.788  0.792  0.807  0.782  0.818
##  [9281]  0.834  0.815  0.817  0.811  0.776  0.764  0.781  0.763  0.773  0.767
##  [9291]  0.772  0.763  0.762  0.785  0.768  0.776  0.771  0.763  0.762  0.762
##  [9301]  0.764  0.763  0.762  0.764  0.762  0.766  0.762  0.871  0.763  0.855
##  [9311]  0.779  0.848  0.821  0.831  0.811  0.777  0.784  0.902  0.764  0.766
##  [9321]  0.761  0.762  0.764  0.778  0.762  0.765  0.762  0.766  0.804  0.768
##  [9331]  0.768  0.784  0.767  0.765  0.777  0.774  0.780  0.800  0.762  0.761
##  [9341]  0.766  0.761  0.782  0.791  0.810  0.792  0.797  0.776  0.771  0.786
##  [9351]  0.788  0.763  0.800  0.801  0.770  0.763  0.765  0.768  0.776  0.773
##  [9361]  0.769  0.797  0.772  0.776  0.761  0.774  0.794  0.770  0.773  0.798
##  [9371]  0.761  0.765  0.807  0.805  0.768  0.772  0.809  0.805  0.762  0.770
##  [9381]  0.773  0.762  0.775  0.764  0.784  0.761  0.792  0.782  0.772  0.793
##  [9391]  0.770  0.780  0.764  0.788  0.803  0.774  0.770  0.775  0.766  0.763
##  [9401]  0.775  0.779  0.763  0.776  0.782  0.764  0.778  0.782  0.762  0.774
##  [9411]  0.790  0.762  0.762  0.762  0.762  0.771  0.762  0.767  0.762  0.766
##  [9421]  0.762  0.761  0.762  0.761  0.761  0.894  0.921  1.057  1.107  1.097
##  [9431]  1.068  1.101  1.132  1.062  0.843  0.762  0.762  0.762  0.762  0.762
##  [9441]  0.764  0.761  0.763  0.762  0.762  0.762  0.762  0.772  0.769  0.784
##  [9451]  0.770  0.770  0.769  0.774  0.761  0.761  0.761  0.763  0.763  0.763
##  [9461]  0.763  0.763  0.761  0.761  0.953  0.923  0.924  0.963  0.801  0.762
##  [9471]  0.893  0.829  0.762  0.809  0.770  0.772  0.794  0.801  0.779  0.781
##  [9481]  0.762  0.769  0.762  0.762  0.762  0.762  0.761  0.762  0.762  0.761
##  [9491]  0.761  0.761  0.761  0.761  0.776  0.773  0.771  0.776  0.771  0.783
##  [9501]  0.772  0.771  0.772  0.771  0.772  0.766  0.772  0.770  0.769  0.766
##  [9511]  0.775  0.768  0.774  0.777  0.772  1.023  0.979  0.932  0.849  0.975
##  [9521]  0.940  0.821  0.772  0.783  0.773  0.785  0.794  0.778  0.778  0.777
##  [9531]  0.778  0.780  0.792  0.814  0.798  0.883  0.849  0.835  0.848  0.902
##  [9541]  0.872  0.897  0.839  0.845  0.839  0.845  0.857  0.852  0.837  0.848
##  [9551]  0.825  0.814  0.843  0.822  0.796  0.832  0.798  0.832  0.816  0.811
##  [9561]  0.804  0.784  0.816  0.789  0.785  0.782  0.799  0.787  0.799  0.796
##  [9571]  0.789  0.780  0.775  0.796  0.837  0.761  0.789  0.876  0.766  0.797
##  [9581]  0.765  0.836  0.818  0.822  0.762  0.762  0.845  0.762  0.895  0.869
##  [9591]  0.801  0.762  0.908  0.762  0.761  0.834  0.845  0.816  0.828  0.838
##  [9601]  0.815  0.813  0.800  0.817  0.824  0.767  0.822  0.841  0.833  0.839
##  [9611]  0.829  0.828  0.837  0.845  0.821  0.777  0.766  0.764  1.217  1.524
##  [9621]  1.218  1.250  1.294  1.265  1.175  1.124  1.246  1.080  1.162  1.117
##  [9631]  1.185  1.128  1.124  1.205  1.114  1.117  1.065  2.371  2.893  2.777
##  [9641]  3.497  3.529  2.143  1.739  1.964  1.736  2.037  2.069  1.584  1.128
##  [9651]  1.096  1.189  1.185  1.904  0.761  0.761  0.761  0.761  0.761  0.761
##  [9661]  1.417  1.860  0.761  0.761  6.832  8.015  7.050  6.124  4.640  2.892
##  [9671]  0.878  0.876  0.874  0.863  0.872  0.832  0.864  0.853  0.863  0.872
##  [9681]  0.865  0.877  0.860  0.886  1.523  1.629  1.661  1.664  1.489  0.831
##  [9691]  0.775  0.781  0.819  0.786  0.882  0.904  0.855  0.769  0.886  0.886
##  [9701]  0.855  0.776  0.808  0.793  0.765  0.766  0.804  0.805  0.761  0.761
##  [9711]  0.761  0.761  0.762  0.761  0.761  0.762  0.764  0.762  0.761  0.789
##  [9721]  0.762  0.771  0.762  0.764  0.789  0.857  0.766  0.774  0.761  0.765
##  [9731]  0.808  0.805  0.761  0.924  0.774  0.836  0.870  0.891  0.814  1.174
##  [9741]  0.999  1.064  0.935  1.033  0.774  0.810  0.795  0.761  0.794  0.824
##  [9751]  0.854  0.767  0.770  0.767  1.012  0.970  1.046  1.048  0.849  1.111
##  [9761]  1.087  1.114  1.127  0.830  0.870  0.891  1.150  0.810  0.839  0.811
##  [9771]  0.871  0.824  0.863  1.388  0.842  0.887  0.988  0.778  0.855  0.800
##  [9781]  0.798  0.826  0.797  0.812  0.875  0.824  0.807  0.811  0.899  0.857
##  [9791]  0.782  0.971  0.780  0.927  0.862  0.782  0.859  0.779  0.917  0.782
##  [9801]  0.950  0.778  0.940  0.778  0.977  0.778  0.785  1.448  1.344  1.125
##  [9811]  1.122  1.142  1.112  1.162  1.113  1.096  1.140  2.148  0.843  0.858
##  [9821]  0.824  0.792  0.819  0.806  0.839  0.865  0.789  0.794  0.792  0.789
##  [9831]  0.784  0.787  0.787  0.799  0.785  0.781  0.796  0.791  0.783  0.809
##  [9841]  0.761  1.526  1.186  1.128  1.350  1.177  1.208  1.252  1.279  1.095
##  [9851]  1.114  1.049  1.083  1.176  1.092  1.030  0.961  0.932  1.213  1.155
##  [9861]  1.106  1.146  1.200  1.082  1.176  0.979  0.982  0.989  1.126  1.007
##  [9871]  0.939  1.108  1.028  0.973  0.818  0.785  1.051  1.215  1.410  0.861
##  [9881]  1.265  1.250  1.317  1.052  1.243  1.173  1.053  1.058  1.222  1.268
##  [9891]  1.275  1.532  1.964  1.028  1.464  0.824  0.830  1.199  0.792  0.848
##  [9901]  0.798  1.271  1.131  0.785  1.458  1.270  1.243  1.237  1.078  0.999
##  [9911]  0.861  1.003  1.017  1.054  0.977  1.019  1.014  1.024  1.113  1.109
##  [9921]  1.159  0.896  0.801  0.852  1.889  0.950  1.222  1.081  1.017  1.086
##  [9931]  0.806  0.915  1.240  1.116  1.240  1.173  1.092  1.118  1.030  0.989
##  [9941]  0.761  1.361  1.242  1.258  0.837  0.839  0.772  1.159  1.049  1.055
##  [9951]  1.261  0.988  0.864  0.869  0.992  0.828  0.825  0.768  0.796  0.818
##  [9961]  0.858  0.787  0.895  0.778  0.860  0.828  0.822  0.761  0.761  0.761
##  [9971]  0.761  0.761  0.761  1.239  1.376  1.150  1.438  1.136  1.178  1.317
##  [9981]  1.869  1.414  1.448  1.554  0.999  1.199  1.166  1.204  1.120  1.237
##  [9991]  1.249  1.316  1.255  1.303  1.296  1.253  1.329  1.281  1.112  1.077
## [10001]  1.409  1.277  1.321  0.866  0.816  0.837  0.927  0.897  0.913  0.889
## [10011]  0.871  0.866  0.872  0.825  0.854  0.926  0.973  0.991  0.865  0.826
## [10021]  0.857  0.893  0.941  0.904  0.873  0.947  0.969  0.963  0.955  0.924
## [10031]  0.904  0.927  0.988  0.902  0.914  1.002  0.973  0.762  1.175  1.198
## [10041]  1.102  1.062  0.914  0.986  0.935  1.162  0.761  0.811  0.801  0.789
## [10051]  1.225  1.219  2.179  1.704  1.797  1.455  1.561  0.951  1.825  1.100
## [10061]  0.948  1.287  0.952  0.964  1.250  1.047  1.173  1.062  1.132  1.180
## [10071]  1.012  1.102  1.030  1.103  1.018  1.020  1.011  1.018  0.761  1.002
## [10081]  0.762  1.032  0.761  0.962  0.762  1.018  0.761  1.034  0.761  1.219
## [10091]  0.761  1.012  0.761  1.023  0.764  1.028  0.761  1.389  0.761  1.837
## [10101]  0.764  1.324  0.762  1.138  0.762  2.038  0.761  1.770  0.761  1.444
## [10111]  0.761  1.779  0.761  1.405  0.761  1.451  0.761  1.421  1.919  0.813
## [10121]  1.040  0.818  0.825  0.904  0.885  1.365  0.967  0.935  0.967  1.258
## [10131]  1.229  1.021  1.220  1.330  1.225  0.971  1.302  1.120  1.078  1.079
## [10141]  0.950  0.779  0.975  1.367  1.502  1.486  1.660  1.635  1.273  1.469
## [10151]  1.376  1.212  0.836  0.893  1.264  1.243  0.913  1.328  1.287  1.259
## [10161]  0.910  0.877  1.283  1.207  1.113  1.307  1.296  1.203  0.990  1.805
## [10171]  1.342  1.404  1.312  1.313  1.307  1.251  0.834  0.827  0.806  0.797
## [10181]  1.230  1.362  1.354  1.347  1.172  0.889  0.826  0.829  0.860  0.761
## [10191]  0.761  0.761  0.761  0.761  1.052  1.138  1.174  0.761  0.954  0.761
## [10201]  1.091  0.761  1.304  0.762  1.332  0.761  1.221  0.761  1.227  1.226
## [10211]  1.069  1.105  1.244  1.260  1.213  1.203  0.761  0.787  0.761  0.917
## [10221]  0.979  0.821  0.762  0.763  0.761  0.763  0.761  0.761  0.762  0.762
## [10231]  0.761  0.762  0.762  0.761  0.762  0.761  0.762  0.761  0.761  0.761
## [10241]  0.761  0.761  0.761  0.761  0.771  0.896  1.170  0.983  0.943  0.923
## [10251]  0.775  0.872  1.093  0.937  0.765  1.131  1.015  1.139  0.789  0.949
## [10261]  0.966  0.890  0.977  0.788  0.845  0.838  0.775  0.842  0.799  0.866
## [10271]  1.224  0.861  1.121  0.761  0.813  0.816  0.800  0.772  0.779  0.818
## [10281]  0.833  0.792  0.815  0.781  0.797  0.793  0.795  0.812  0.794  0.881
## [10291]  0.802  0.941  0.802  0.861  0.813  0.883  0.805  0.874  0.795  0.838
## [10301]  0.846  0.850  0.785  0.844  0.798  0.831  0.824  0.847  0.810  0.965
## [10311]  0.847  0.778  0.778  0.775  0.821  0.786  0.767  0.780  0.777  0.767
## [10321]  0.763  0.766  0.772  0.761  0.778  0.812  0.776  0.769  0.773  0.779
## [10331]  0.783  0.809  0.796  0.824  0.799  0.800  0.792  0.813  0.810  1.019
## [10341]  1.093  1.270  1.301  1.196  1.106  1.164  1.044  1.105  1.090  1.301
## [10351]  2.025  1.243  1.420  1.322  1.484  1.428  1.365  1.372  1.153  2.226
## [10361]  1.458  1.259  1.416  1.473  1.637  1.473  1.103  1.278  1.342  0.897
## [10371]  1.229  1.146  0.857  1.270  1.418  1.738  1.216  1.363  1.037  1.272
## [10381]  1.286  1.402  1.827  1.428  1.750  1.723  1.581  1.604  1.604  1.894
## [10391]  1.995  1.423  1.451  1.255  1.147  1.276  1.275  1.369  1.504  1.368
## [10401]  1.308  1.401  1.615  1.083  1.528  0.988  1.191  1.636  1.570  1.120
## [10411]  0.803  0.787  0.769  0.765  0.766  0.776  0.792  0.761  0.767  0.848
## [10421]  0.761  1.016  0.764  1.134  0.864  1.127  0.890  1.061  0.826  0.986
## [10431]  1.685  1.785  0.776  1.272  1.350  1.338  0.876  1.118  0.868  0.767
## [10441]  0.783  0.786  0.780  0.795  0.855  0.849  0.802  0.868  0.885  0.817
## [10451]  0.815  0.775  0.762  0.761  1.882  1.703  1.134  1.005  1.445  1.317
## [10461]  1.073  0.995  1.266  1.359  1.179  1.147  1.151  1.149  1.229  1.498
## [10471]  1.287  1.052  1.025  0.897  1.121  1.203  1.252  1.171  1.236  1.142
## [10481]  1.338  1.094  0.889  0.996  1.031  0.851  0.972  1.057  1.310  1.269
## [10491]  0.992  1.095  1.192  1.143  1.203  1.113  1.190  0.899  1.145  1.183
## [10501]  1.249  1.258  1.115  1.043  1.013  1.102  1.172  1.156  0.992  0.989
## [10511]  0.765  0.841  0.928  0.767  0.806  1.429  0.765  0.765  0.762  1.493
## [10521]  0.761  0.761  0.765  0.762  0.762  0.762  0.765  0.761  0.761  0.944
## [10531]  0.761  0.765  0.765  1.641  2.219  1.550  0.975  0.797  0.785  0.989
## [10541]  0.878  0.906  1.651  1.645  1.966  1.408  0.815  0.769  0.769  0.769
## [10551]  0.765  0.774  0.766  0.870  1.534  0.885  0.765  0.780  0.789  0.812
## [10561]  0.856  0.912  0.841  0.799  0.893  0.925  1.018  0.960  0.972  1.124
## [10571]  1.021  0.977  1.094  0.839  0.917  0.853  0.788  0.861  1.023  1.180
## [10581]  1.063  1.260  1.137  1.321  1.315  1.150  0.924  0.864  0.839  0.876
## [10591]  0.913  1.017  0.922  1.050  1.425  1.082  0.928  1.006  0.866  1.098
## [10601]  1.070  1.051  1.116  1.028  1.080  1.202  0.863  1.057  1.046  1.774
## [10611]  1.581  2.157  2.379  2.570  1.447  1.583  1.186  1.055  1.326  1.479
## [10621]  1.294  1.280  0.940  0.998  1.038  1.049  1.053  0.972  0.763  0.763
## [10631]  0.763  0.763  0.763  0.761  0.762  0.761  0.763  0.763  0.763  0.763
## [10641]  0.763  0.763  0.763  0.763  0.763  0.763  0.763  0.901  0.761  0.830
## [10651]  0.761  0.950  0.761  0.980  0.763  1.051  0.763  1.608  0.761  1.870
## [10661]  0.761  2.792  0.761  1.755  0.761  1.198  0.761  1.422  0.762  1.572
## [10671]  0.761  1.519  0.761  1.188  0.761  1.050  0.761  1.063  0.761  0.980
## [10681]  0.762  1.695  0.761  1.311  0.761  1.598  0.761  0.763  0.761  0.761
## [10691]  0.761  0.761  0.761  0.763  0.761  0.761  0.761  0.761  0.761  0.761
## [10701]  0.761  0.761  0.761  0.763  0.761  0.761  0.761  0.763  0.763  0.763
## [10711]  0.998  1.213  1.565  2.601  2.785  2.281  1.741  2.324  2.323  1.992
## [10721]  1.904  2.449  1.937  3.336  0.761  0.931  1.191  1.260  0.965  1.101
## [10731]  0.874  1.063  1.069  0.868  1.179  1.137  1.197  0.915  1.016  1.087
## [10741]  1.055  1.715  1.477  1.058  1.227  1.099  0.997  1.712  2.001  1.234
## [10751]  1.228  1.255  1.363  1.442  1.400  1.667  1.858  1.729  1.949  2.110
## [10761]  1.735  1.604  1.691  1.707  1.642  1.365  1.700  1.678  0.764  0.761
## [10771]  0.761  1.539  1.157  0.761  1.116  0.798  1.035  0.873  0.913  0.880
## [10781]  0.761  0.761  0.761  0.929  1.014  0.761  1.082  1.039  0.978  1.055
## [10791]  1.075  1.830  1.072  1.085  0.978  1.054  0.971  0.944  0.901  0.986
## [10801]  1.002  1.002  1.051  1.066  1.068  1.379  1.352  1.011  0.761  0.873
## [10811]  0.761  0.761  0.790  1.331  1.467  1.218  2.316  1.800  0.989  1.565
## [10821]  1.328  1.732  1.615  1.373  1.619  1.503  1.270  1.800  1.705  1.491
## [10831]  1.080  0.994  1.002  1.130  0.762  0.761  0.761  0.833  0.804  1.279
## [10841]  1.112  1.035  1.106  1.241  1.232  2.087  2.668  2.327  1.754  1.709
## [10851]  1.430  1.938  1.247  1.903  2.341  1.692  1.778  1.609  1.493  1.445
## [10861]  1.974  1.294  1.178  0.761  0.761  0.761  0.761  0.762  1.089  0.915
## [10871]  1.176  1.159  1.143  3.370  1.154  1.119  1.108  1.034  1.193  1.169
## [10881]  1.191  1.140  1.293  1.196  1.977  0.964  2.504  3.531  1.736  1.425
## [10891]  1.281  1.271  1.960  1.385  1.280  1.433  2.544  1.486  1.857  1.516
## [10901]  1.946  1.397  1.577  1.600  1.731  1.236  1.503  1.758  1.345  1.403
## [10911]  2.263  1.596  2.161  1.299  1.622  1.398  1.833  0.951  1.694  1.439
## [10921]  1.950  0.773  1.871  1.296  1.645  0.878  2.165  2.428  2.134  1.255
## [10931]  1.378  1.362  1.370  1.319  1.404  1.283  1.275  1.921  1.764  1.422
## [10941]  0.768  0.761  0.818  0.761  0.964  0.894  0.773  0.854  0.957  0.761
## [10951]  0.761  0.899  2.089  0.965  1.390  0.992  1.018  1.009  1.008  1.040
## [10961]  1.169  0.989  1.307  0.991  1.398  0.914  1.034  1.030  1.000  1.285
## [10971]  1.259  1.310  1.277  1.186  1.170  1.300  1.190  1.164  1.517  0.984
## [10981]  0.986  0.925  0.879  0.866  0.906  0.823  0.982  0.779  1.049  0.801
## [10991]  0.783  1.148  1.081  1.145  0.800  1.416  0.761  1.160  1.159  0.992
## [11001]  0.965  0.765  0.779  0.763  0.798  1.071  1.077  2.215  1.558  1.902
## [11011]  2.691  1.651  1.862  2.102  1.783  1.780  1.768  2.278  2.009  1.779
## [11021]  1.898  1.662  1.673  1.808  2.003  1.925  1.760  2.487  2.063  0.933
## [11031]  1.466  1.186  1.134  1.133  0.978  1.446  1.193  1.657  1.402  1.082
## [11041]  1.276  1.058  1.026  1.036  1.159  1.227  0.958  1.054  1.183  1.177
## [11051]  1.241  1.027  0.837  0.949  0.920  0.911  1.037  0.938  0.963  0.924
## [11061]  1.065  1.109  1.087  1.129  1.135  1.076  1.050  0.920  0.934  0.998
## [11071]  1.006  1.063  0.875  0.965  0.980  0.843  1.036  0.907  1.284  0.761
## [11081]  1.451  1.386  1.316  1.193  0.987  1.148  1.029  1.089  1.366  1.531
## [11091]  1.029  1.425  1.048  1.072  0.818  0.761  1.042  1.089  1.105  0.914
## [11101]  1.048  1.106  1.055  0.995  1.044  0.990  1.043  1.066  0.761  0.770
## [11111]  0.826  0.817  0.814  0.793  0.898  1.343  0.852  0.818  1.573  1.574
## [11121]  0.985  1.003  0.761  1.554  0.845  0.939  1.103  1.553  1.658  1.038
## [11131]  1.675  0.845  1.086  0.999  1.294  1.414  0.896  1.503  0.917  1.430
## [11141]  1.546  1.141  1.507  1.466  1.175  0.947  0.761  0.761  1.339  0.881
## [11151]  0.908  1.619  1.882  0.887  0.810  1.218  1.188  1.011  0.953  1.030
## [11161]  1.140  1.074  1.033  0.794  1.169  0.855  0.912  0.905  0.824  0.796
## [11171]  0.856  0.852  0.802  0.786  0.784  0.817  0.833  0.761  0.761  0.958
## [11181]  1.004  1.145  0.765  0.790  0.945  1.144  0.765  0.765  0.762  0.765
## [11191]  1.557  1.243  1.404  1.339  0.762  0.822  0.946  1.224  1.140  1.135
## [11201]  1.235  0.823  1.486  1.229  1.302  1.443  1.791  2.525  1.741  2.043
## [11211]  1.317  1.255  1.251  2.190  0.981  1.362  1.389  1.386  1.232  0.765
## [11221]  0.763  0.766  0.765  0.763  0.763  0.763  0.784  0.891  0.897  1.300
## [11231]  0.962  0.945  1.087  0.924  1.045  1.044  1.003  0.871  0.918  0.895
## [11241]  0.864  0.848  0.974  0.855  0.762  0.762  0.762  0.762  0.761  0.816
## [11251]  0.761  0.768  0.762  0.762  0.762  0.779  0.765  2.671  0.762  2.155
## [11261]  0.762  1.663  0.766  1.628  0.761  1.555  1.612  1.739  1.623  1.703
## [11271]  1.712  1.164  1.150  1.403  1.347  0.762  1.428  0.761  0.762  0.761
## [11281]  0.761  0.761  0.762  0.762  0.761  0.762  0.762  0.762  0.940  1.543
## [11291]  1.549  0.775  0.871  1.628  0.789  0.810  0.765  0.762  0.765  0.761
## [11301]  0.976  0.880  0.867  0.857  0.761  0.785  0.794  0.925  1.284  1.200
## [11311]  1.315  0.785  1.675  1.396  1.580  0.835  0.851  0.813  0.805  0.795
## [11321]  0.761  0.763  1.542  1.569  1.518  0.833  0.792  0.848  0.770  1.602
## [11331]  1.591  0.834  1.415  0.781  0.805  1.521  1.604  0.815  1.587  0.851
## [11341]  1.368  1.510  0.927  1.633  0.790  0.868  0.828  0.809  0.771  0.800
## [11351]  0.776  0.861  0.853  0.977  0.901  0.799  0.860  0.827  0.879  0.815
## [11361]  0.879  0.827  0.925  0.821  0.852  0.837  0.795  0.846  0.879  0.853
## [11371]  0.877  0.862  0.767  0.781  0.765  0.769  0.798  0.779  0.791  0.855
## [11381]  0.953  0.830  0.765  0.762  0.767  0.765  0.771  0.765  0.763  0.770
## [11391]  0.773  0.766  0.766  0.767  0.763  0.762  0.764  0.763  0.769  0.765
## [11401]  0.767  0.768  0.768  0.771  0.775  0.770  0.805  0.807  0.761  0.761
## [11411]  0.866  0.769  0.801  0.782  0.763  0.765  0.763  0.771  0.768  0.779
## [11421]  0.770  0.816  0.816  0.771  0.779  0.793  0.802  0.815  0.771  0.768
## [11431]  0.776  0.770  0.783  0.803  1.051  0.856  0.795  0.788  0.930  1.288
## [11441]  0.761  0.760  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [11451]  0.761  0.787  0.761  0.870  0.761  0.761  0.761  0.761  0.761  1.048
## [11461]  1.480  2.654  1.225  0.884  0.824  1.002  0.955  0.784  0.922  0.936
## [11471]  0.881  1.396  0.908  0.963  1.158  1.033  0.940  0.945  0.789  0.863
## [11481]  0.847  0.794  0.828  0.797  0.782  0.824  1.502  2.009  1.254  1.455
## [11491]  1.512  1.172  2.251  1.504  0.793  0.764  0.796  0.762  0.761  0.776
## [11501]  0.761  0.761  0.762  0.762  0.761  0.761  0.761  0.761  0.761  0.761
## [11511]  0.761  0.761  0.761  0.824  0.775  0.793  0.807  0.783  0.774  0.783
## [11521]  0.764  0.819  0.766  0.766  0.811  0.799  0.762  0.808  0.766  0.775
## [11531]  0.763  0.762  0.857  0.762  0.761  0.763  0.769  0.766  0.762  1.059
## [11541]  0.828  0.891  0.943  0.861  0.869  1.290  1.971  0.828  0.783  0.764
## [11551]  0.778  0.806  0.833  0.830  0.867  0.875  0.835  0.815  0.867  0.870
## [11561]  0.984  0.842  1.093  0.999  0.935  0.829  0.896  0.955  1.480  3.972
## [11571]  0.761  0.761  0.761  0.982  0.761  0.866  0.907  0.954  0.931  0.898
## [11581]  0.899  1.119  0.877  0.825  0.956  0.781  0.803  0.802  0.777  0.890
## [11591]  0.783  0.960  0.762  0.761  0.761  0.761  1.959  1.246  1.686  1.147
## [11601]  1.444  1.291  1.252  1.302  0.874  0.836  0.896  0.806  0.778  0.782
## [11611]  0.774  0.779  1.029  1.133  1.098  1.243  1.305  1.159  1.270  1.115
## [11621]  1.682  1.453  0.762  0.797  0.764  0.771  0.851  0.927  0.904  1.462
## [11631]  1.910  1.935  0.761  0.889  1.071  0.892  1.110  0.943  0.761  0.762
## [11641]  0.761  0.761  0.761  0.761  1.293  1.521  1.285  1.340  1.341  1.335
## [11651]  1.726  1.315  0.761  1.319  1.405  1.612  1.191  1.177  1.291  1.327
## [11661]  1.180  1.297  1.182  1.132  1.379  1.363  1.372  1.237  1.487  1.361
## [11671]  1.348  0.761  0.761  0.761  0.761  0.875  0.931  1.315  1.131  1.130
## [11681]  1.145  0.777  4.348  0.956  1.035  0.832  0.983  2.294  1.209  1.706
## [11691]  2.326  0.933  1.284  1.367  2.714  1.410  2.672  1.435  1.113  1.812
## [11701]  1.614  0.762  0.768  0.768  0.765  0.841  0.879  0.890  0.799  0.766
## [11711]  0.820  0.769  0.781  0.794  0.771  0.763  0.774  0.777  0.783  0.777
## [11721]  0.773  0.793  0.799  0.782  0.781  0.786  0.774  0.772  0.848  0.977
## [11731]  0.847  0.927  0.762  0.994  0.762  0.761  0.853  0.761  0.775  0.763
## [11741]  0.916  0.851  1.599  1.551  1.084  0.787  0.832  0.769  0.928  0.768
## [11751]  2.258  2.491  0.761  0.761  1.158  0.904  0.763  0.766  0.959  1.584
## [11761]  0.974  1.073  1.082  0.762  0.761  0.891  1.027  1.132  1.169  0.761
## [11771]  0.761  1.027  0.761  1.208  1.199  1.160  1.170  1.021  0.761  0.761
## [11781]  1.012  0.899  0.891  1.007  0.977  0.817  0.884  0.867  0.947  0.775
## [11791]  0.823  0.794  0.863  0.912  0.788  0.831  0.784  0.775  0.791  0.925
## [11801]  1.390  1.202  0.818  0.761  0.761  0.762  0.761  0.761  0.761  0.762
## [11811]  0.761  0.841  0.761  0.761  0.761  0.954  0.761  0.761  0.761  0.761
## [11821]  1.169  1.158  1.341  1.279  1.752  1.414  4.375  2.072  1.672  1.676
## [11831]  0.925  0.828  0.857  0.936  0.795  0.834  0.826  0.789  0.887  0.792
## [11841]  0.788  0.889  0.918  1.068  1.104  0.933  0.761  0.761  0.869  0.761
## [11851]  0.786  0.832  0.891  0.834  0.869  0.852  0.867  1.229  0.840  0.970
## [11861]  1.280  0.822  1.438  0.762  0.762  0.767  0.770  0.762  0.763  0.777
## [11871]  0.765  0.787  0.769  0.770  0.762  0.763  0.802  0.775  0.780  0.899
## [11881]  1.193  1.507  1.087  0.762  0.769  0.761  0.765  0.789  0.761  0.762
## [11891]  0.816  0.919  0.904  0.926  0.979  0.915  1.020  1.061  0.888  0.822
## [11901]  1.050  0.817  1.077  0.983  2.416  1.644  0.962  0.934  0.881  0.889
## [11911]  0.935  1.056  0.937  0.898  0.869  0.875  0.868  1.736  0.778  0.828
## [11921]  0.932  0.854  0.851  0.960  0.844  0.980  1.033  1.286  1.025  1.380
## [11931]  0.982  0.989  0.949  0.849  0.900  1.031  1.015  0.856  0.984  2.309
## [11941]  0.901  1.059  1.136  4.256  2.977  0.978  0.795  1.020  0.989  1.041
## [11951]  1.159  1.155  0.790  1.207  1.277  1.429  1.399  0.892  1.212  0.852
## [11961]  1.065  0.905  0.860  0.763  0.762  0.787  0.788  0.788  0.790  0.793
## [11971]  0.761  0.822  0.808  0.788  0.783  0.775  0.766  0.792  0.765  0.778
## [11981]  0.767  0.780  0.784  0.800  0.790  0.935  0.853  0.810  0.796  0.819
## [11991]  0.779  0.789  1.116  0.893  0.867  0.829  1.008  1.012  1.229  1.221
## [12001]  0.999  1.103  0.799  0.773  0.779  0.797  0.801  0.882  0.901  0.841
## [12011]  1.044  1.035  0.913  0.905  0.879  1.024  1.074  2.863  3.173  2.860
## [12021]  2.710  0.835  0.852  1.227  0.859  1.234  0.821  0.826  2.447  1.498
## [12031]  0.761  0.817  0.761  1.939  1.277  1.324  0.762  0.799  0.792  1.006
## [12041]  0.761  0.828  0.853  0.761  0.776  1.049  1.063  2.774  2.038  0.762
## [12051]  0.933  0.968  0.914  0.762  0.762  0.911  1.448  1.097  0.776  0.774
## [12061]  0.770  0.768  0.775  0.772  0.772  0.773  0.769  0.768  0.765  0.764
## [12071]  0.765  0.764  0.764  0.763  0.764  0.763  0.763  0.764  0.764  0.789
## [12081]  0.779  0.782  0.767  0.777  0.786  1.067  1.096  0.994  1.096  1.119
## [12091]  1.279  1.320  1.054  1.130  1.213  1.227  1.027  0.876  1.169  1.112
## [12101]  1.017  0.941  0.916  0.816  0.766  0.762  0.843  1.130  0.838  0.870
## [12111]  0.858  0.892  0.811  1.926  0.761  1.945  1.988  1.633  1.496  1.375
## [12121]  1.524  1.410  1.524  1.474  1.410  2.082  1.507  2.113  2.174  1.551
## [12131]  1.431  1.810  1.412  1.124  1.097  1.287  0.761  0.761  0.761  0.761
## [12141]  0.761  0.761  0.761  1.120  0.761  0.762  0.774  0.774  0.798  0.932
## [12151]  1.232  1.475  1.454  0.947  0.985  0.841  0.838  1.171  1.229  1.370
## [12161]  0.763  0.762  0.762  0.762  0.762  0.763  0.764  0.762  0.763  0.762
## [12171]  0.762  0.762  0.764  0.766  0.764  0.765  0.765  0.763  0.763  0.769
## [12181]  0.765  0.766  0.770  0.769  0.763  0.766  0.769  0.766  0.761  0.762
## [12191]  0.761  0.762  0.761  0.761  0.764  0.765  0.762  0.857  0.764  0.762
## [12201]  0.800  0.767  0.795  0.830  0.767  0.792  0.761  0.793  0.762  0.762
## [12211]  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.770  0.767  0.911
## [12221]  0.815  0.907  0.792  0.857  0.762  0.807  0.771  0.770  0.763  0.785
## [12231]  0.769  0.845  0.789  0.810  0.883  0.836  0.860  0.800  0.764  0.774
## [12241]  0.781  0.768  0.763  0.956  0.813  0.846  0.827  0.784  1.048  1.142
## [12251]  1.159  0.875  0.936  0.993  0.809  0.785  0.793  0.804  0.783  0.807
## [12261]  0.858  0.822  0.809  0.856  0.922  0.941  0.836  0.970  1.009  0.890
## [12271]  0.882  0.795  0.761  0.762  0.761  0.798  0.761  0.761  0.761  0.761
## [12281]  0.761  0.761  0.761  0.761  0.761  0.881  0.761  0.764  0.768  0.766
## [12291]  0.763  0.761  0.761  0.768  0.767  0.767  0.778  0.772  0.771  0.764
## [12301]  0.765  0.776  0.766  0.762  0.761  1.601  1.415  1.111  1.231  1.371
## [12311]  3.307  2.694  1.931  1.930  0.944  0.803  0.787  0.783  0.767  0.767
## [12321]  2.112  0.816  0.950  0.761  0.998  0.762  0.774  0.763  1.515  1.567
## [12331]  0.826  1.359  1.453  0.857  1.411  1.625  1.600  1.391  0.873  1.374
## [12341]  1.874  1.684  1.581  1.239  1.140  1.173  1.355  1.095  0.761  0.871
## [12351]  0.934  1.050  1.098  1.048  1.060  0.761  0.761  0.761  0.828  0.835
## [12361]  0.975  1.064  0.761  0.781  0.761  0.802  0.801  0.900  0.894  0.867
## [12371]  0.891  0.785  1.283  2.674  1.047  1.007  1.062  1.217  1.164  2.288
## [12381]  0.761  0.761  0.762  0.769  0.766  0.878  0.957  1.034  0.834  0.826
## [12391]  0.930  0.761  0.762  0.786  0.762  0.813  0.818  0.762  0.773  0.762
## [12401]  0.817  0.936  0.958  0.874  0.822  0.793  0.789  0.886  0.918  3.084
## [12411]  4.436  0.899  0.799  0.766  0.772  1.051  1.124  0.827  1.044  0.958
## [12421]  0.794  0.790  0.838  0.838  0.796  0.827  0.840  0.837  0.790  0.844
## [12431]  0.765  0.762  0.761  0.761  0.761  0.761  0.763  0.762  0.761  0.761
## [12441]  0.761  0.767  0.792  0.761  0.761  0.761  0.761  0.762  0.762  0.761
## [12451]  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.908  0.761
## [12461]  0.761  1.439  1.219  1.494  0.763  1.411  0.763  0.763  0.892  0.846
## [12471]  1.121  1.090  1.449  1.199  1.029  1.178  0.763  0.762  0.764  0.766
## [12481]  0.765  0.764  0.764  0.763  0.764  0.761  0.761  0.761  0.761  0.761
## [12491]  0.761  0.762  0.765  0.762  0.761  0.761  0.762  0.762  0.761  0.762
## [12501]  0.764  0.763  0.761  0.816  2.833  4.152  2.977  2.133  0.761  0.761
## [12511]  0.761  0.761  1.278  1.213  1.262  0.761  0.860  1.234  1.316  0.869
## [12521]  0.869  0.805  0.791  1.261  1.298  1.112  0.764  0.764  0.873  0.766
## [12531]  0.763  0.873  1.106  1.173  1.105  0.842  1.149  0.992  0.867  0.818
## [12541]  1.100  0.830  0.798  0.813  0.804  0.829  0.917  0.826  0.825  0.818
## [12551]  1.202  0.828  0.808  0.809  0.823  0.852  0.805  0.800  0.787  0.804
## [12561]  0.814  0.785  0.798  0.804  0.787  0.809  0.816  0.824  0.802  0.761
## [12571]  1.036  0.966  0.955  1.047  1.034  1.009  1.032  0.762  0.762  0.762
## [12581]  0.762  1.121  0.764  0.766  0.763  0.816  0.761  0.761  0.761  0.761
## [12591]  0.761  0.791  0.863  0.839  0.842  0.834  0.814  0.847  0.797  0.802
## [12601]  0.767  0.858  0.802  0.824  0.812  0.856  0.813  0.808  0.789  0.800
## [12611]  0.777  0.771  0.773  0.779  0.786  0.781  0.795  0.797  0.789  0.811
## [12621]  0.894  0.762  0.784  0.781  0.784  0.807  0.784  0.762  0.789  0.762
## [12631]  1.120  0.815  0.801  0.795  0.792  0.792  0.797  0.791  0.789  0.782
## [12641]  0.794  0.802  0.818  0.802  0.808  0.837  1.045  0.785  0.795  0.790
## [12651]  0.790  0.787  1.174  0.866  1.159  0.858  0.836  1.177  0.797  1.009
## [12661]  0.831  1.186  1.098  1.002  0.824  0.856  0.835  0.864  0.878  0.889
## [12671]  0.850  0.808  0.996  0.777  0.788  0.778  0.792  0.793  0.787  0.787
## [12681]  0.783  0.771  0.784  0.776  0.828  0.792  0.781  0.805  0.828  0.805
## [12691]  0.805  0.773  1.087  0.779  1.075  0.773  0.915  0.770  0.960  0.799
## [12701]  1.032  0.802  0.948  0.805  0.899  0.799  0.835  0.788  0.835  0.829
## [12711]  0.821  0.817  0.805  0.826  0.831  0.911  0.806  0.807  0.792  0.784
## [12721]  0.783  1.067  0.761  0.761  0.761  0.761  0.809  0.801  0.761  0.762
## [12731]  0.835  0.789  0.761  0.786  1.019  0.780  0.787  0.799  0.819  0.819
## [12741]  0.819  0.798  0.782  0.778  0.782  0.791  0.780  0.788  0.788  0.791
## [12751]  0.777  0.776  0.776  1.052  0.761  0.761  0.761  0.762  0.793  0.766
## [12761]  0.813  0.840  1.095  1.165  0.838  1.071  0.823  0.974  0.983  0.904
## [12771]  0.812  0.822  0.798  0.843  0.805  0.784  0.789  0.797  0.793  0.795
## [12781]  0.782  0.787  0.783  0.783  0.786  0.781  0.773  0.796  0.792  0.863
## [12791]  0.843  0.848  0.812  0.826  0.838  0.816  0.800  0.816  0.817  0.804
## [12801]  0.823  0.818  0.870  0.845  0.853  0.829  0.797  0.784  1.093  0.764
## [12811]  0.761  0.775  0.764  0.811  0.813  0.793  0.833  0.846  0.828  0.823
## [12821]  0.849  0.853  0.812  0.808  0.849  0.838  0.841  0.841  0.849  0.858
## [12831]  0.858  0.849  0.860  0.767  0.808  1.056  0.761  0.761  0.773  0.784
## [12841]  0.807  0.808  0.795  0.794  0.835  0.796  0.817  0.849  0.832  1.148
## [12851]  0.816  1.061  1.103  1.106  1.117  0.815  1.088  0.779  1.079  1.193
## [12861]  0.848  1.014  0.788  0.800  0.803  0.787  0.779  0.843  0.797  0.799
## [12871]  0.809  0.891  0.844  0.812  0.794  0.814  0.818  0.886  0.788  0.830
## [12881]  0.793  0.803  0.780  0.774  0.768  0.798  0.793  0.776  0.816  0.790
## [12891]  0.795  0.778  0.797  0.763  0.799  0.792  1.074  0.951  0.895  1.148
## [12901]  1.098  1.139  1.176  1.272  1.183  1.221  0.836  0.780  0.788  0.766
## [12911]  0.831  0.798  0.822  0.951  0.842  0.817  0.817  1.182  0.799  0.816
## [12921]  0.893  0.807  0.799  0.815  0.912  0.956  1.014  0.845  0.930  0.955
## [12931]  0.920  0.835  0.828  0.812  0.821  0.854  0.922  0.871  0.874  0.849
## [12941]  0.900  0.846  0.830  0.859  0.823  0.861  0.843  0.816  0.834  0.829
## [12951]  0.789  0.762  0.764  0.774  0.764  0.764  0.763  0.839  0.781  0.785
## [12961]  0.789  0.791  0.789  0.784  0.776  0.776  0.772  0.796  0.787  0.792
## [12971]  0.787  0.799  0.774  0.779  0.768  0.762  0.763  0.767  0.763  0.762
## [12981]  0.762  0.761  0.766  0.763  0.825  0.862  0.887  0.903  0.857  0.865
## [12991]  0.872  0.803  0.810  0.806  0.833  0.810  0.808  0.814  0.823  0.836
## [13001]  0.877  0.827  0.785  0.788  0.804  0.800  0.797  0.772  0.812  0.819
## [13011]  0.831  0.814  0.836  0.980  0.823  0.994  0.936  0.908  0.985  0.942
## [13021]  0.919  0.888  0.873  0.909  0.887  0.876  0.910  0.828  0.811  0.829
## [13031]  0.864  0.812  0.889  0.832  0.864  0.823  0.895  0.901  0.853  0.821
## [13041]  0.807  0.778  0.789  0.794  0.784  0.782  0.791  0.889  0.873  0.845
## [13051]  0.837  0.854  0.816  0.899  0.807  0.815  0.841  0.857  0.829  0.815
## [13061]  0.845  0.851  0.862  0.856  0.841  0.776  0.810  0.771  0.789  0.780
## [13071]  0.804  0.787  0.803  0.788  1.049  0.833  0.920  0.825  0.823  0.848
## [13081]  0.792  0.885  0.940  0.885  0.931  0.924  0.895  0.880  0.867  0.853
## [13091]  0.857  0.829  0.852  0.852  0.826  0.934  0.895  0.883  0.892  0.819
## [13101]  0.861  0.805  0.879  0.819  0.775  0.809  0.856  0.875  0.845  0.817
## [13111]  0.807  0.821  0.834  0.825  0.879  0.835  0.827  0.877  0.856  0.840
## [13121]  0.813  0.827  0.819  0.821  0.804  0.814  0.836  0.816  0.816  0.833
## [13131]  0.840  0.808  0.856  0.894  0.836  0.880  0.845  0.821  0.875  0.830
## [13141]  0.768  0.783  0.785  0.776  0.778  0.803  0.785  0.800  0.807  0.801
## [13151]  0.792  0.787  0.832  0.792  0.789  0.786  0.784  0.791  0.792  0.795
## [13161]  0.791  0.811  0.781  0.772  0.774  0.777  0.785  0.798  0.779  0.786
## [13171]  0.766  0.779  0.777  0.765  0.774  0.775  0.777  0.781  0.802  0.816
## [13181]  0.808  0.802  0.808  0.801  0.804  0.801  0.787  0.786  0.787  0.779
## [13191]  0.787  0.796  0.787  0.793  0.794  0.795  0.780  0.769  0.770  0.770
## [13201]  0.772  0.774  0.774  0.771  0.781  0.775  0.973  0.784  0.791  0.788
## [13211]  0.811  0.836  0.786  0.957  0.825  0.822  0.869  0.819  0.821  0.808
## [13221]  0.797  0.807  0.791  0.790  0.895  0.820  0.818  0.815  0.873  0.876
## [13231]  0.788  0.857  0.831  0.790  0.848  0.822  0.794  0.801  0.815  0.782
## [13241]  0.799  0.795  0.802  0.808  0.798  0.822  0.810  0.808  0.811  0.795
## [13251]  0.804  0.800  0.794  0.804  0.804  0.799  0.792  0.803  0.805  0.776
## [13261]  0.784  0.791  0.815  0.826  0.799  0.808  0.819  0.808  0.817  0.784
## [13271]  0.805  0.806  0.863  0.818  0.828  0.800  0.829  0.853  0.787  1.026
## [13281]  0.930  0.927  0.907  0.875  0.836  0.835  0.815  0.826  0.785  0.784
## [13291]  0.807  0.806  0.830  0.828  0.841  0.804  0.829  0.856  0.858  0.805
## [13301]  0.843  0.813  0.789  0.798  0.817  0.797  0.801  0.798  0.796  0.802
## [13311]  0.894  0.923  0.877  0.824  0.859  0.819  0.847  0.858  0.807  0.788
## [13321]  0.785  0.806  0.774  0.785  0.805  0.789  0.802  0.800  0.778  0.785
## [13331]  0.802  0.780  0.796  0.817  0.851  0.776  0.779  0.779  0.765  0.772
## [13341]  0.774  0.772  0.777  0.813  0.825  0.806  0.799  0.799  0.807  0.814
## [13351]  0.812  0.801  0.827  0.813  0.797  0.806  0.826  0.817  0.796  0.808
## [13361]  0.821  0.806  0.773  0.774  0.776  0.786  0.771  0.776  0.765  0.774
## [13371]  0.772  0.885  0.913  0.871  0.964  0.929  0.954  0.939  0.848  0.855
## [13381]  0.861  0.852  0.811  0.834  0.861  0.841  0.863  0.861  0.799  0.776
## [13391]  0.815  0.822  0.802  0.777  0.774  0.766  0.767  0.772  0.770  0.776
## [13401]  0.797  0.843  0.813  0.845  0.826  0.816  0.844  0.805  0.798  0.775
## [13411]  0.787  0.780  0.775  0.775  0.774  0.792  0.777  0.781  0.770  0.774
## [13421]  0.773  0.774  0.766  0.771  0.765  0.765  0.770  0.769  0.786  0.892
## [13431]  0.787  0.804  0.835  0.811  0.792  0.798  0.785  0.790  0.782  0.780
## [13441]  0.825  0.787  0.782  0.793  0.836  0.863  0.823  0.787  0.806  0.834
## [13451]  0.807  0.875  0.818  0.855  0.808  0.802  0.822  0.816  0.834  0.829
## [13461]  0.820  0.807  0.810  0.804  0.807  0.812  0.847  0.798  0.805  0.799
## [13471]  0.808  0.796  0.798  0.805  0.794  0.795  0.803  0.793  0.773  0.795
## [13481]  0.802  0.813  0.828  0.843  0.814  0.871  0.886  0.787  0.949  0.934
## [13491]  0.909  0.933  0.932  0.900  0.895  0.841  0.876  0.863  0.900  0.889
## [13501]  0.925  0.878  0.905  0.949  0.880  0.858  0.864  0.860  0.837  0.859
## [13511]  0.834  0.811  0.825  0.837  1.087  0.802  0.828  0.816  0.859  0.824
## [13521]  0.834  0.912  0.884  0.878  0.932  0.981  0.861  0.823  0.805  0.826
## [13531]  0.796  0.818  0.835  0.831  0.833  0.863  0.901  0.907  0.861  0.888
## [13541]  0.857  0.843  0.830  0.819  0.940  0.865  0.848  0.888  0.864  0.924
## [13551]  0.921  0.909  0.847  0.828  0.954  0.940  0.884  0.917  0.942  0.883
## [13561]  0.891  0.826  0.846  0.815  0.841  0.852  0.850  0.855  0.851  0.852
## [13571]  0.867  0.830  0.910  0.829  0.867  0.823  0.830  0.882  0.922  0.833
## [13581]  1.082  0.827  0.900  0.853  0.877  0.817  0.781  0.930  0.832  0.918
## [13591]  0.871  0.857  0.878  0.816  0.825  0.826  0.828  0.844  0.848  0.845
## [13601]  0.866  0.851  0.884  0.859  0.826  0.858  0.884  0.811  0.912  0.829
## [13611]  0.814  0.861  0.870  0.959  0.886  0.981  0.975  0.931  0.982  0.804
## [13621]  0.903  0.812  0.825  0.828  0.976  0.952  0.965  0.979  0.926  0.979
## [13631]  0.974  1.010  0.987  0.973  0.957  0.939  0.869  0.862  0.913  0.930
## [13641]  0.956  0.898  0.920  0.808  0.934  0.925  0.812  0.886  0.871  0.893
## [13651]  0.864  0.924  0.854  0.891  0.860  0.860  0.856  0.875  0.872  0.813
## [13661]  0.827  0.824  0.838  0.840  0.840  0.834  0.828  0.804  0.817  0.836
## [13671]  0.834  0.854  0.829  0.867  0.852  0.873  0.850  0.869  0.850  0.768
## [13681]  0.836  0.780  0.805  0.827  0.909  0.889  0.861  0.891  0.886  0.885
## [13691]  0.835  0.796  0.799  0.854  0.820  0.821  0.838  0.830  0.835  0.840
## [13701]  0.868  0.825  0.797  0.822  0.800  0.791  0.775  0.789  0.764  0.795
## [13711]  0.804  4.327  5.747  5.744  6.663  4.367  4.265  3.975  3.942  4.179
## [13721]  3.183  3.926  3.933  3.301  3.645  3.479  4.090  3.835  4.712  4.228
## [13731]  4.228  1.839  4.087  3.290  2.275  1.971  1.515  2.487  4.031  0.762
## [13741]  1.212  1.532  0.762  1.458  0.762  1.641  1.496  1.553  2.499  2.023
## [13751]  2.120  0.930  0.764  1.571  1.870  1.846  1.396  1.920  1.792  2.482
## [13761]  2.161  2.737  2.806  2.750  1.518  0.762  2.399  1.360  0.878  0.822
## [13771]  0.825  0.843  0.829  0.819  0.792  0.805  0.802  0.786  0.825  0.816
## [13781]  0.798  0.915  1.331  1.076  0.881  1.143  0.765  0.761  0.762  0.762
## [13791]  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762
## [13801]  0.762  0.762  0.762  0.762  0.762  0.762  0.761  0.761  0.761  0.761
## [13811]  0.761  0.761  0.761  0.761  0.761  0.763  0.763  0.760  0.815  0.999
## [13821]  0.781  0.768  0.761  1.421  1.175  1.180  1.527  0.761  0.761  1.648
## [13831]  0.973  1.190  0.762  1.545  0.762  1.973  1.066  2.005  0.844  2.066
## [13841]  0.762  2.205  0.762  1.783  0.762  1.833  0.762  1.058  0.762  0.761
## [13851]  0.841  1.646  1.387  1.808  1.365  1.840  1.656  2.108  1.221  1.788
## [13861]  1.312  1.525  0.868  0.970  0.895  0.760  0.824  0.834  0.873  1.196
## [13871]  0.867  1.157  0.989  1.950  1.113  2.569  2.296  1.503  1.123  1.458
## [13881]  0.761  1.243  1.243  1.233  1.767  1.318  1.730  1.434  1.390  1.504
## [13891]  1.269  1.705  1.352  1.506  1.799  1.729  1.531  1.673  1.266  1.027
## [13901]  0.947  0.840  1.216  1.147  1.005  1.124  1.075  1.066  1.030  0.779
## [13911]  0.777  0.781  0.779  0.790  0.793  0.785  0.775  0.772  0.779  0.765
## [13921]  0.767  0.780  0.773  0.766  0.976  0.761  0.761  0.761  0.761  0.761
## [13931]  0.762  0.761  0.762  0.762  0.762  0.760  0.761  0.966  0.760  0.766
## [13941]  0.762  0.808  0.760  0.792  0.760  0.762  0.760  0.767  0.760  0.764
## [13951]  0.760  0.760  0.760  0.761  0.761  0.762  0.761  0.761  0.761  0.761
## [13961]  0.761  0.761  0.761  0.761  0.761  0.761  0.941  0.773  0.776  0.761
## [13971]  0.761  0.761  0.761  0.761  0.761  0.761  0.762  0.762  0.762  0.762
## [13981]  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762
## [13991]  0.761  0.761  0.762  0.761  0.761  0.822  0.762  0.762  0.761  0.761
## [14001]  0.761  0.761  0.761  1.366  0.762  0.762  0.761  0.761  0.761  0.761
## [14011]  0.761  0.761  1.595  0.875  0.761  0.761  0.766  1.541  0.762  0.762
## [14021]  0.762  0.762  0.762  0.762  3.043  1.229  1.741  0.859  1.955  0.762
## [14031]  0.762  0.762  0.761  0.761  0.762  0.761  0.761  0.762  0.762  0.762
## [14041]  0.761  0.761  0.761  0.761  0.761  0.762  0.762  0.769  0.761  0.761
## [14051]  0.761  0.761  0.798  0.811  0.761  0.761  0.839  0.812  0.762  0.798
## [14061]  0.762  0.800  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762
## [14071]  0.762  0.762  0.761  0.761  0.762  0.761  0.761  0.762  0.762  0.762
## [14081]  0.761  0.761  0.761  0.761  0.761  1.519  1.043  0.837  0.761  0.761
## [14091]  0.880  0.761  1.541  2.276  1.464  1.816  1.739  1.571  1.983  1.227
## [14101]  0.762  1.940  0.762  1.594  1.843  1.756  0.971  0.909  0.774  0.761
## [14111]  0.769  0.761  0.777  0.761  0.776  0.761  0.772  0.761  0.769  0.762
## [14121]  0.773  0.777  0.797  0.781  0.761  0.787  0.761  0.813  0.761  0.782
## [14131]  0.764  0.792  0.970  0.761  0.761  0.761  0.761  0.761  0.761  0.760
## [14141]  0.761  0.762  0.816  0.762  0.762  0.762  0.762  0.761  0.762  0.761
## [14151]  0.761  0.762  0.761  0.761  0.761  0.920  0.761  0.762  0.775  0.761
## [14161]  0.789  0.921  0.943  0.957  0.966  0.944  0.829  0.939  0.902  0.952
## [14171]  0.900  0.943  0.953  0.949  0.908  0.908  0.761  0.761  0.761  0.761
## [14181]  0.761  0.762  0.761  0.762  0.762  0.762  0.761  0.957  0.941  0.946
## [14191]  0.875  0.942  1.034  1.953  2.180  1.072  1.245  0.847  3.274  1.189
## [14201]  1.149  1.319  0.778  0.799  0.782  0.779  0.784  0.774  0.790  0.761
## [14211]  0.784  0.761  0.800  1.221  0.794  0.761  0.862  0.868  0.845  0.847
## [14221]  0.811  0.957  0.915  0.773  0.924  0.761  0.839  0.835  0.761  0.761
## [14231]  0.799  0.818  0.813  0.805  0.827  0.784  0.799  0.803  0.842  0.790
## [14241]  0.782  0.789  0.761  0.762  0.771  0.789  0.776  1.265  0.777  0.934
## [14251]  0.783  0.761  0.763  0.761  0.771  0.761  0.806  2.332  0.797  1.479
## [14261]  0.812  1.464  0.771  0.878  0.806  0.767  0.768  0.765  0.784  1.496
## [14271]  0.763  0.763  0.762  1.079  0.762  0.761  0.762  0.761  0.761  0.761
## [14281]  0.805  0.764  1.417  1.202  1.564  1.549  1.688  2.058  0.761  0.762
## [14291]  0.763  0.762  0.762  0.762  0.762  0.765  0.762  0.761  0.761  0.762
## [14301]  0.761  0.761  0.762  0.761  0.762  0.761  0.777  0.787  0.795  0.801
## [14311]  0.787  0.800  0.828  0.816  0.803  0.808  0.820  0.790  0.802  0.813
## [14321]  0.840  0.865  0.761  0.761  0.761  0.761  0.761  0.762  0.761  0.762
## [14331]  0.762  0.762  0.761  0.855  0.798  0.799  0.815  0.786  1.067  1.162
## [14341]  1.121  1.138  1.135  1.131  1.160  1.186  1.122  1.088  1.140  1.127
## [14351]  1.079  0.762  1.129  0.762  0.761  0.870  0.761  0.760  0.761  0.761
## [14361]  0.761  0.761  0.761  0.761  0.761  0.762  0.761  0.761  0.762  0.761
## [14371]  0.761  0.761  0.764  0.761  0.762  1.511  0.761  0.762  0.762  0.762
## [14381]  0.762  0.762  0.762  0.761  0.762  0.762  0.762  0.761  0.762  0.761
## [14391]  0.761  0.762  1.065  0.761  1.072  1.123  0.761  1.276  1.438  0.760
## [14401]  0.761  0.762  0.763  0.762  0.762  0.762  0.762  0.761  0.762  0.761
## [14411]  0.761  0.762  0.761  0.761  0.761  0.761  0.762  0.761  1.002  0.762
## [14421]  0.761  0.762  0.762  0.762  0.762  4.353  8.238  2.898  5.960  1.663
## [14431]  1.168  0.770  0.983  0.993  0.875  0.780  0.764  0.772  0.774  0.772
## [14441]  0.763  0.764  0.764  0.762  0.762  0.762  0.762  0.763  0.764  0.789
## [14451]  0.765  0.942  0.762  0.801  0.769  0.781  0.819  0.770  0.762  0.762
## [14461]  0.762  3.831  7.223  2.179  2.172  2.733  1.778  3.007  2.739  3.861
## [14471]  4.323  5.778  4.822  2.890  2.680  2.051  2.812  2.180  5.758  2.142
## [14481]  3.706  6.758  4.337 11.107  1.856  1.721  1.361  3.869  1.371  1.216
## [14491]  0.817  0.762  0.761  0.763  5.272  6.878  1.523  1.679  0.877  3.506
## [14501]  2.368  1.662  4.287  2.632  0.958  0.901  1.771  3.610  0.761  4.940
## [14511]  0.952  0.761  0.761  0.761  0.761  1.435  0.761  1.509  0.948  0.762
## [14521]  0.762  0.762  0.762  2.136 10.288  5.504  0.761  1.264  1.010  0.761
## [14531]  0.761  0.761  0.761  3.825  2.962  0.761  8.253  2.152  2.014  3.858
## [14541]  3.813  2.646  6.501  3.706  1.958  1.662  1.875  0.762  1.672  4.986
## [14551]  8.326  6.771  3.151  3.925  1.135  1.360  1.240  1.156  0.761  6.202
## [14561]  6.093  3.201  5.867  2.927  3.934  3.625  4.717  2.414  5.743  2.253
## [14571]  3.027  4.868  1.672  2.686  1.291  5.197  3.668  3.049  2.354  3.948
## [14581]  4.113  4.665  5.629  5.294  2.669  5.755  1.286  3.243  1.808  2.908
## [14591]  6.710  8.595  6.606  3.964  4.516  3.395  3.610  3.104  1.914  2.905
## [14601]  1.512  2.685  0.819  1.182  1.958  2.745  1.886  0.872  3.156  3.407
## [14611]  5.436  5.951  3.141  4.263  4.465  3.914  3.212  2.939  1.540  0.762
## [14621]  0.762  0.761  0.761  0.761  0.761  0.761  0.762  1.091  0.769  0.958
## [14631]  0.811  1.025  1.039  0.761  1.141  0.775  0.761  0.925  0.761  0.761
## [14641]  0.761  0.869  0.761  1.062  0.891  0.796  0.969  0.778  1.017  0.875
## [14651]  1.441  1.594  1.546  1.516  1.514  0.760  0.820  1.916  0.761  0.761
## [14661]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [14671]  0.761  1.164  2.279  0.762  2.051  0.762  0.762  0.762  0.762  0.762
## [14681]  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.761  0.916  2.236
## [14691]  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.761  0.761  0.761
## [14701]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [14711]  0.761  0.761  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762
## [14721]  0.762  0.762  0.762  0.762  0.765  0.761  0.761  0.826  0.762  0.762
## [14731]  0.762  0.762  0.762  0.762  0.762  0.815  0.762  0.761  0.761  0.761
## [14741]  1.111  1.330  1.411  0.827  0.761  0.771  0.761  1.054  0.762  0.982
## [14751]  0.762  0.761  0.800  0.761  0.807  0.863  0.811  0.761  0.803  0.761
## [14761]  0.811  0.761  0.810  0.797  0.794  0.807  0.762  0.762  0.762  0.762
## [14771]  0.762  0.762  0.762  0.762  0.762  0.761  0.761  0.761  0.932  0.761
## [14781]  1.147  0.761  1.116  0.956  1.156  0.761  1.075  1.332  1.134  0.936
## [14791]  1.081  1.130  1.128  1.132  1.020  1.110  1.157  1.123  1.119  1.143
## [14801]  0.866  1.109  1.018  1.404  0.896  1.191  0.772  1.136  0.761  1.106
## [14811]  0.761  1.119  0.761  1.143  0.815  1.003  0.797  0.993  0.773  1.077
## [14821]  0.800  1.006  0.776  1.820  0.959  1.892  1.329  4.095  2.290  1.982
## [14831]  4.089  1.011  0.851  0.761  0.761  1.033  0.761  1.124  0.761  1.095
## [14841]  1.045  1.108  1.031  1.046  1.073  0.764  1.051  1.109  1.115  1.023
## [14851]  0.761  1.157  0.901  1.172  0.862  1.123  0.761  1.087  0.761  0.761
## [14861]  0.761  0.761  0.761  0.761  0.770  1.067  1.145  1.154  1.141  1.069
## [14871]  1.094  0.762  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [14881]  0.762  0.761  0.762  2.096  1.496  2.075  1.185  0.762  0.761  0.761
## [14891]  0.761  0.761  0.761  0.761  0.848  0.761  0.761  0.761  0.761  1.030
## [14901]  3.820  3.376  2.338  1.185  0.761  0.761  0.761  0.761  0.761  0.761
## [14911]  0.761  0.761  0.761  0.761  0.761  0.761  1.022  2.371  3.153  2.001
## [14921]  3.050  2.861  1.892  0.997  0.781  1.035  1.517  0.760  0.762  0.761
## [14931]  0.761  0.762  0.761  0.762  0.761  0.761  0.761  0.761  0.761  0.762
## [14941]  0.761  0.762  0.761  0.761  0.761  0.761  0.761  0.761  0.762  0.762
## [14951]  0.761  0.761  0.762  0.761  0.761  0.762  0.761  0.761  0.761  0.761
## [14961]  0.761  0.762  0.761  0.762  0.760  0.760  0.761  0.760  0.762  0.761
## [14971]  0.760  0.762  0.761  0.760  0.761  0.761  0.760  0.762  0.760  0.761
## [14981]  0.762  0.760  0.761  0.760  0.761  0.761  0.760  0.761  0.762  0.760
## [14991]  0.761  1.133  0.760  0.760  0.761  0.761  0.761  0.761  0.761  0.760
## [15001]  0.761  0.761  0.761  0.761  0.760  0.760  0.760  0.761  0.761  0.761
## [15011]  0.761  0.762  0.761  0.761  0.762  0.761  0.761  0.761  0.762  0.763
## [15021]  0.761  0.761  0.761  0.761  0.760  0.763  0.761  0.761  0.763  0.762
## [15031]  0.761  0.763  0.761  0.762  0.766  0.761  0.766  0.761  0.766  0.761
## [15041]  1.832  0.761  1.336  0.761  0.761  0.761  0.766  0.761  0.761  0.761
## [15051]  0.762  0.761  0.762  0.762  0.762  0.761  0.761  0.760  2.000  0.761
## [15061]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [15071]  0.761  1.313  0.824  0.760  0.844  0.761  0.761  2.546  0.762  3.286
## [15081]  0.762  0.761  0.761  0.762  0.761  0.762  0.761  0.761  0.761  0.761
## [15091]  0.760  0.761  1.375  0.760  0.762  1.375  0.760  0.761  0.761  0.760
## [15101]  0.761  0.763  0.760  0.760  0.761  0.761  1.083  0.761  0.761  0.761
## [15111]  0.761  0.760  0.761  0.762  0.761  0.761  0.762  0.761  0.761  0.762
## [15121]  0.761  0.761  0.762  0.763  0.761  0.761  0.761  0.766  0.762  2.777
## [15131]  1.312  3.636  0.938  0.911  0.913  0.945  0.922  0.976  0.906  0.822
## [15141]  0.787  0.867  0.871  0.980  0.912  0.907  0.864  0.918  0.912  0.887
## [15151]  0.969  0.760  0.760  0.760  0.760  0.760  0.760  0.760  0.760  1.491
## [15161]  0.840  1.006  0.811  0.972  0.926  0.870  0.761  0.903  0.761  0.867
## [15171]  0.762  0.928  0.761  0.925  0.760  0.911  0.762  0.905  0.762  0.940
## [15181]  0.762  0.894  0.761  0.951  0.761  0.951  0.761  0.908  0.761  0.970
## [15191]  0.761  0.906  0.761  0.860  0.761  0.909  0.761  0.933  0.761  0.928
## [15201]  0.766  0.974  0.998  0.845  0.938  0.967  0.952  0.968  1.522  2.140
## [15211]  1.287  1.938  0.762  0.761  0.761  0.762  0.761  0.760  0.762  0.761
## [15221]  0.761  0.761  0.761  0.952  0.925  0.967  0.943  0.927  0.856  0.867
## [15231]  0.953  0.884  0.761  0.923  0.888  0.862  0.827  0.935  0.857  0.916
## [15241]  0.889  0.925  0.925  0.915  0.952  0.880  0.917  0.815  0.882  0.916
## [15251]  0.772  0.835  0.931  0.845  0.807  0.926  0.949  0.801  0.783  0.926
## [15261]  0.938  0.945  0.777  0.879  0.931  0.777  0.761  0.905  0.763  0.762
## [15271]  0.763  0.846  0.761  0.761  0.925  0.809  0.835  0.761  0.925  0.760
## [15281]  0.760  0.784  0.763  0.760  0.762  0.761  0.763  0.760  0.762  0.761
## [15291]  0.763  0.760  0.764  0.774  0.763  0.760  0.761  0.800  0.782  0.760
## [15301]  0.876  0.770  0.797  0.760  1.809  0.763  0.760  0.837  0.761  0.760
## [15311]  0.761  0.767  1.237  0.773  0.761  0.760  0.761  0.841  1.284  0.780
## [15321]  1.307  0.829  1.373  0.782  0.798  0.798  0.761  0.805  0.760  0.761
## [15331]  0.895  0.833  0.761  1.150  0.765  0.761  0.761  0.773  0.935  0.761
## [15341]  0.762  0.761  0.807  0.834  0.797  0.761  0.762  0.832  0.762  0.762
## [15351]  0.762  0.761  0.839  0.761  0.780  0.762  0.760  0.867  0.772  0.764
## [15361]  0.762  0.762  0.896  0.770  0.807  0.762  0.762  0.788  0.762  0.761
## [15371]  0.762  0.762  0.807  0.761  0.828  0.761  0.818  0.763  0.878  0.761
## [15381]  0.804  0.762  0.910  0.761  0.879  0.761  0.933  0.761  0.820  0.761
## [15391]  0.922  0.761  0.768  0.782  0.846  0.761  0.852  0.811  0.958  0.761
## [15401]  0.928  0.761  0.971  0.761  0.825  0.781  0.921  0.761  0.870  0.761
## [15411]  0.893  0.766  0.824  0.780  0.935  0.924  0.913  0.856  0.859  0.940
## [15421]  0.933  0.846  0.884  0.950  0.873  0.791  0.926  0.802  0.761  0.764
## [15431]  0.762  0.762  0.782  0.761  1.376  0.782  0.792  0.794  0.865  0.918
## [15441]  0.844  0.760  0.760  0.937  0.760  0.931  0.940  0.760  0.954  0.866
## [15451]  0.760  0.800  0.866  0.866  1.095  1.525  0.831  0.778  0.760  1.220
## [15461]  0.762  1.009  0.798  0.762  0.846  0.889  0.762  0.830  0.908  0.761
## [15471]  0.761  1.380  0.814  0.761  0.829  0.761  2.053  0.864  0.761  1.342
## [15481]  0.873  0.761  1.474  0.800  0.761  1.411  0.815  0.761  1.314  0.761
## [15491]  0.766  0.940  0.867  0.906  0.927  0.898  0.914  0.930  0.902  0.920
## [15501]  0.929  0.883  0.868  0.918  0.889  0.926  0.894  0.942  0.942  0.760
## [15511]  0.760  0.958  0.762  0.761  0.761  0.761  0.766  0.932  0.903  0.919
## [15521]  2.797  4.270  6.197  3.766  3.937  3.400  2.105  2.117  1.982  1.912
## [15531]  1.826  1.208  1.951  2.383  1.239  2.418  1.520  2.177  1.982  2.304
## [15541]  1.230  1.271  1.335  1.292  1.886  0.762  3.224  2.928  3.258  3.273
## [15551]  2.824  1.145  0.915  1.060  1.218  2.260  2.377  0.761  4.008  0.761
## [15561]  7.483  0.761 10.194  0.761  7.092  1.507  8.171  1.199  7.961  2.512
## [15571]  8.048  0.761  6.367  0.761  5.661  5.727  2.974  3.568  3.919  6.383
## [15581]  2.240  0.761  0.761  0.761  0.761  0.761  0.761  0.762  1.152  1.060
## [15591]  1.068  1.051  1.082  1.097  1.029  1.085  1.006  1.007  1.018  0.926
## [15601]  0.938  1.821  1.018  0.968  1.815  1.786  1.668  2.364  1.704  0.761
## [15611]  0.761  0.761  0.761  0.761  0.761  0.761  1.224  1.168  1.219  0.896
## [15621]  1.254  1.276  1.243  2.690  0.761  1.191  2.251  1.835  1.288  3.220
## [15631]  1.284  1.211  1.327  1.020  2.057  1.563  1.211  0.969  2.515  2.494
## [15641]  1.193  0.888  6.026  1.728  0.911  1.011  5.446  2.549  1.036  0.983
## [15651]  1.534  2.477  0.908  0.888  2.150  0.875  1.335  1.298  1.834  0.925
## [15661]  0.891  1.226  2.661  1.018  0.950  1.680  2.407  1.349  0.957  1.235
## [15671]  1.896  0.867  1.050  1.142  1.633  0.997  0.963  2.424  1.617  0.937
## [15681]  0.869  1.941  1.088  0.881  0.871  1.052  1.605  0.999  1.176  1.236
## [15691]  1.666  1.001  0.858  1.499  1.906  0.851  0.875  1.193  1.594  1.867
## [15701]  0.875  2.387  1.078  0.924  0.874  2.354  1.032  0.843  0.858  1.926
## [15711]  1.834  0.961  0.866  2.310  1.862  0.950  0.870  1.859  1.793  1.590
## [15721]  1.915  3.120  2.501  2.035  1.899  2.789  1.369  2.249  2.491  3.089
## [15731]  0.762  1.475  0.766  3.157  1.990  1.840  1.938  2.064  1.344  1.940
## [15741]  1.523  1.108  3.729  1.977  0.982  2.219  1.608  1.497  0.995  0.761
## [15751]  1.883  0.933  1.292  0.898  1.047  1.640  3.160  1.309  1.058  0.761
## [15761]  2.179  1.907  1.856  3.524  1.191  0.965  1.539  5.260  2.735  0.966
## [15771]  1.481  5.987  2.045  1.519  1.528  5.331  2.187  0.911  1.430  6.991
## [15781]  1.517  0.847  0.936  5.009  2.530  1.099  0.940  4.542  1.530  0.761
## [15791]  0.947  2.980  2.813  0.865  0.951  1.615  2.493  0.827  0.917  1.528
## [15801]  1.641  1.172  0.966  1.505  2.162  0.820  0.879  1.534  1.283  0.764
## [15811]  1.190  1.530  1.904  1.062  0.953  0.873  1.343  1.406  0.761  1.072
## [15821]  0.761  0.941  2.087  1.056  0.761  0.982  0.761  1.026  1.182  1.019
## [15831]  0.761  1.002  0.761  1.145  0.761  1.034  0.761  0.912  0.761  1.036
## [15841]  1.238  1.575  0.761  0.939  1.849  0.906  2.477  1.530  1.195  1.054
## [15851]  0.762  1.039  1.228  1.072  1.232  1.337  1.316  1.380  2.840  2.536
## [15861]  2.294  3.693  3.082  3.394  2.465  2.180  3.043  3.703  3.192  3.673
## [15871]  3.130  3.101  3.422  2.900  3.472  3.299  2.863  3.122  2.281  2.662
## [15881]  2.926  2.280  1.575  0.761  1.909  1.971  3.102  2.304  1.248  6.410
## [15891]  1.634  1.001  2.913  2.238  1.072  3.631  1.277  0.973  0.761  1.432
## [15901]  0.979  3.439  0.990  0.950  3.186  1.295  0.872  3.703  1.278  0.761
## [15911]  4.658  1.102  0.991  5.929  1.139  0.973  3.439  1.286  2.347  1.011
## [15921]  0.994  0.846  1.498  1.030  1.241  1.128  2.731  0.764  1.431  4.574
## [15931]  1.742  1.970  1.520  2.832  1.721  5.992  1.477  1.496  0.761  1.496
## [15941]  1.522  1.496  0.762  0.761  1.618  0.761  0.761  1.218  0.761  1.623
## [15951]  1.060  2.267  1.456  1.564  1.242  1.251  0.996  1.655  1.977  3.533
## [15961]  2.102  2.501  2.856  2.883  2.716  2.779  2.438  2.111  2.869  2.911
## [15971]  1.587  1.412  2.269  1.398  1.484  3.349  3.366  2.444  0.761  1.902
## [15981]  1.481  1.525  1.341  2.239  2.001  0.761  1.516  1.496  0.761  0.761
## [15991]  0.761  0.845  0.987  0.761  0.896  1.007  0.761  0.761  1.676  0.761
## [16001]  0.892  1.010  0.761  0.908  1.029  0.762  0.761  1.329  1.181  0.761
## [16011]  1.070  1.029  0.761  0.992  0.791  0.761  1.073  0.999  0.761  0.978
## [16021]  1.042  0.761  2.994  1.082  0.761  1.862  1.012  0.763  2.735  0.835
## [16031]  0.761  2.434  1.173  0.761  2.538  0.820  0.761  0.975  0.851  0.761
## [16041]  3.063  0.761  0.761  0.822  0.805  0.761  2.024  0.761  0.798  0.761
## [16051]  2.292  0.831  0.761  0.761  2.982  0.815  0.762  0.922  3.153  0.761
## [16061]  0.961  0.761  1.118  3.020  0.848  1.449  0.948  1.380  0.761  1.163
## [16071]  6.871  1.687  1.574  0.851  1.401  1.626  1.278  3.692  1.785  3.801
## [16081]  2.259  0.877  1.919  1.044  0.904  2.386  2.142  1.247  1.181  3.859
## [16091]  0.962  0.997  0.805  0.761  0.944  0.802  0.761  1.036  0.810  0.761
## [16101]  1.161  0.826  0.761  1.184  0.908  0.761  1.085  1.182  0.761  1.036
## [16111]  1.675  0.761  1.058  0.882  0.940  0.765  0.932  0.761  0.790  0.822
## [16121]  0.761  0.870  0.761  0.911  0.833  0.815  0.888  0.849  0.809  0.995
## [16131]  0.778  0.868  0.761  0.761  1.142  0.761  0.988  1.110  0.761  0.761
## [16141]  0.983  0.761  0.761  0.817  0.762  0.762  0.876  0.793  0.775  0.801
## [16151]  0.891  0.887  0.777  0.801  0.848  0.803  0.805  0.765  0.768  0.769
## [16161]  0.770  0.769  0.773  0.768  0.777  0.804  0.842  0.839  0.776  0.764
## [16171]  0.763  0.767  0.768  0.769  0.769  0.768  0.772  0.766  0.766  0.765
## [16181]  0.764  0.764  0.764  0.765  0.776  0.767  0.774  0.763  0.765  0.765
## [16191]  0.763  0.777  0.765  0.765  0.766  0.764  0.765  0.765  0.786  0.762
## [16201]  0.764  0.763  0.763  0.767  0.763  0.763  0.763  0.763  0.764  0.763
## [16211]  0.763  0.768  0.764  0.763  0.762  0.762  0.771  0.783  0.809  0.854
## [16221]  0.881  0.895  0.899  0.930  0.807  0.805  0.773  0.775  0.812  0.805
## [16231]  0.795  0.790  0.833  0.819  0.807  0.761  0.761  0.761  0.761  0.761
## [16241]  0.793  0.790  0.766  0.766  0.776  0.824  0.779  0.771  0.768  0.762
## [16251]  0.762  0.764  0.777  0.773  0.772  0.766  0.769  0.765  0.770  0.765
## [16261]  0.774  0.774  0.764  0.763  0.790  0.794  0.761  0.774  0.990  0.847
## [16271]  1.051  1.028  0.955  1.037  1.011  1.001  1.111  1.429  1.280  1.402
## [16281]  1.174  1.101  1.371  1.426  1.279  1.655  1.179  1.439  3.014  0.771
## [16291]  0.763  0.765  0.767  0.765  0.768  0.794  0.833  0.826  0.881  0.869
## [16301]  0.846  0.831  0.869  0.784  0.783  0.789  0.786  0.779  0.800  0.794
## [16311]  0.823  0.855  0.761  0.761  0.761  0.761  0.763  0.845  0.904  0.927
## [16321]  0.763  0.762  0.762  0.822  0.823  0.783  0.868  0.858  0.761  0.844
## [16331]  0.790  0.955  0.777  0.894  0.807  0.890  0.862  1.079  0.761  0.761
## [16341]  0.761  0.800  0.762  0.761  0.762  0.764  0.765  0.762  0.762  0.818
## [16351]  0.882  0.914  0.892  0.888  0.900  0.793  0.786  0.768  0.767  0.771
## [16361]  0.762  0.770  0.766  0.770  0.765  0.766  0.770  0.765  0.765  0.900
## [16371]  0.881  0.761  0.761  0.851  0.818  0.849  0.782  0.813  0.854  0.854
## [16381]  0.853  0.882  0.808  0.778  0.763  0.761  0.762  0.820  0.826  0.870
## [16391]  0.871  0.926  0.919  0.928  0.891  0.772  0.816  0.761  0.761  0.796
## [16401]  0.790  0.825  0.834  0.831  0.871  0.821  0.827  0.852  0.986  0.938
## [16411]  0.795  0.888  0.833  0.831  0.867  1.120  0.820  0.881  0.866  0.845
## [16421]  0.841  0.832  0.845  0.858  0.859  0.819  0.842  0.838  0.826  0.852
## [16431]  0.846  0.841  0.855  0.883  0.848  0.906  0.880  0.871  0.876  0.895
## [16441]  0.847  0.919  1.017  0.829  0.814  0.897  0.842  0.774  0.814  0.803
## [16451]  0.839  0.922  0.913  0.951  0.977  0.943  0.948  0.859  1.063  1.027
## [16461]  1.114  0.833  0.905  0.948  1.006  0.826  1.003  1.032  0.975  1.060
## [16471]  0.944  1.019  0.921  0.937  0.902  0.906  0.912  0.953  0.917  0.936
## [16481]  0.957  0.936  0.925  0.944  0.762  0.857  0.916  0.761  0.891  0.911
## [16491]  0.843  0.923  1.000  0.902  0.999  1.065  1.094  0.969  1.064  0.910
## [16501]  0.928  1.018  0.912  1.017  1.060  0.904  1.042  0.999  1.002  1.060
## [16511]  1.124  0.942  1.016  1.199  0.927  1.086  1.054  1.073  1.121  1.085
## [16521]  0.968  0.945  1.076  0.977  1.050  1.036  0.988  1.078  1.086  1.048
## [16531]  1.175  1.064  1.055  1.092  1.326  1.057  1.060  1.171  1.306  1.065
## [16541]  1.251  1.079  1.005  1.327  1.067  1.021  0.971  1.089  1.123  0.925
## [16551]  0.931  0.775  0.964  1.172  1.002  1.005  1.172  1.167  1.518  1.441
## [16561]  1.106  1.079  1.041  1.085  1.105  1.012  1.008  1.024  1.009  1.007
## [16571]  1.074  1.010  1.083  1.040  1.003  1.093  0.761  0.761  0.761  0.761
## [16581]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [16591]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [16601]  0.762  0.863  0.761  0.868  0.761  0.760  0.761  0.761  0.760  0.761
## [16611]  0.831  0.840  0.761  0.825  0.761  0.854  0.761  0.860  0.761  0.847
## [16621]  0.761  0.831  0.761  0.828  0.761  0.838  0.761  0.838  0.761  1.510
## [16631]  0.761  1.299  0.761  1.536  0.761  1.808  0.761  1.460  1.552  1.554
## [16641]  0.761  1.537  1.556  1.563  1.566  1.592  1.610  0.761  1.536  1.600
## [16651]  1.575  1.506  1.537  1.556  1.585  1.554  1.574  1.600  1.556  1.568
## [16661]  1.206  0.994  0.761  1.010  0.761  0.899  0.872  0.880  0.811  0.883
## [16671]  0.889  0.761  0.868  1.035  0.831  0.901  0.854  0.796  0.834  0.761
## [16681]  0.855  0.761  0.885  0.761  0.839  1.007  0.838  0.946  0.844  0.761
## [16691]  0.818  0.778  0.801  0.885  0.972  0.944  0.951  0.972  0.975  0.951
## [16701]  0.899  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [16711]  0.761  0.761  0.761  0.763  0.761  0.761  0.761  0.761  0.761  0.761
## [16721]  0.761  0.761  0.761  0.761  0.770  0.766  0.761  0.761  0.761  0.761
## [16731]  0.761  0.761  0.761  0.762  0.761  0.762  0.761  0.762  0.761  0.762
## [16741]  0.761  0.762  0.761  0.761  0.761  0.762  0.761  0.762  0.761  0.762
## [16751]  0.761  0.762  0.761  0.761  0.761  0.761  0.763  0.762  0.761  0.761
## [16761]  0.761  0.761  0.761  0.761  0.761  0.762  0.761  0.761  0.761  0.762
## [16771]  0.761  0.762  0.761  0.761  0.761  0.761  0.762  0.762  0.762  0.762
## [16781]  0.762  0.762  0.762  0.762  0.762  0.761  0.762  0.762  0.761  0.761
## [16791]  0.761  0.761  0.761  0.824  0.761  0.781  0.762  0.762  0.761  0.762
## [16801]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.762  0.761  0.762
## [16811]  0.761  0.920  0.843  0.867  0.783  0.915  0.799  0.782  0.811  0.860
## [16821]  0.842  0.915  0.837  0.809  0.843  0.855  0.791  0.967  0.906  0.867
## [16831]  1.809  0.907  1.584  0.806  1.673  0.788  1.747  0.867  1.761  0.868
## [16841]  1.754  0.921  1.838  0.941  1.845  0.936  1.794  0.953  1.721  0.975
## [16851]  1.713  1.041  1.967  0.898  1.914  0.933  2.005  0.871  1.635  0.991
## [16861]  1.265  1.902  1.628  1.606  1.462  1.206  1.714  1.901  1.623  1.638
## [16871]  1.514  0.981  0.907  0.860  0.986  0.842  0.888  0.786  0.845  0.785
## [16881]  0.910  0.784  0.951  0.831  0.842  0.861  0.921  0.880  1.084  0.911
## [16891]  0.903  0.896  0.909  0.771  0.954  0.816  0.985  0.830  0.903  0.800
## [16901]  0.899  0.761  0.761  0.861  0.865  0.761  0.761  1.922  1.811  1.437
## [16911]  1.942  1.713  2.046  2.167  1.646  2.132  1.539  2.442  2.140  2.281
## [16921]  2.184  1.103  2.361  2.280  2.179  2.039  1.840  2.814  2.307  2.654
## [16931]  1.861  3.452  2.322  3.316  2.201  3.653  2.260  3.723  2.778  4.404
## [16941]  2.534  5.732  0.907  2.261  2.365  2.593  2.678  2.650  3.306  1.907
## [16951]  4.226  1.762  3.440  2.742  3.663  6.081  2.802  4.764  1.744  1.959
## [16961]  3.817  3.287  5.968  5.266  2.224  2.787  4.480  7.055  5.270  5.713
## [16971]  5.303  3.681  4.717  4.598  2.848  2.966  3.109  3.156  2.149  2.399
## [16981]  2.170  2.131  1.948  1.754  1.414  2.439  1.116  1.668  1.551  1.701
## [16991]  0.800  1.809  2.239  1.639  2.078  2.055  1.990  2.532  2.691  1.126
## [17001]  2.643  2.512  2.457  3.262  2.176  2.457  2.831  1.820  1.738  1.540
## [17011]  1.628  1.777  1.792  1.533  0.762  0.762  0.762  0.762  0.762  0.762
## [17021]  0.762  0.762  0.762  0.813  1.084  1.168  1.588  1.493  1.335  1.374
## [17031]  1.344  1.587  1.212  1.523  0.761  0.762  1.528  0.761  1.294  1.989
## [17041]  2.813  1.344  1.628  1.572  1.484  2.521  5.745  5.690  2.718  5.975
## [17051]  8.509  7.192  5.954  1.774  2.408  1.490  1.702  1.012  1.149  1.263
## [17061]  0.942  1.660  1.884  2.303  1.694  1.190  2.081  2.249  2.181  2.177
## [17071]  2.069  2.241  1.970  1.962  2.166  1.979  2.315  2.551  1.015  2.226
## [17081]  2.924  3.231  3.726  2.565  4.009  3.730  1.693  2.471  1.030  2.132
## [17091]  2.102  2.178  1.635  0.924  1.605  1.684  1.823  1.601  2.066  1.809
## [17101]  1.593  2.401  1.373  2.106  2.309  2.154  2.369  1.878  2.413  2.318
## [17111]  2.137  1.941  2.042  3.060  2.166  3.072  2.008  3.622  2.164  3.535
## [17121]  2.312  3.368  2.348  3.537  2.827  4.070  2.231  2.893  1.108  1.854
## [17131]  2.252  3.087  2.584  3.095  2.072  4.020  4.407  4.442  4.619  3.443
## [17141]  7.920  9.410  1.980  2.305  3.841  2.924  3.970  3.106  6.181  5.258
## [17151]  1.706  3.770  8.613  3.881  2.934  2.011  3.122  1.884  2.828  4.406
## [17161]  1.542  2.369  2.604  2.510  1.738  1.720  2.178  1.798  1.648  1.723
## [17171]  2.037  1.300  1.631  1.519  1.666  2.058  2.074  2.160  1.704  2.075
## [17181]  1.978  2.756  2.973  3.319  3.690  3.336  3.755  2.869  2.280  1.587
## [17191]  3.990  5.722  3.290  5.035  3.311  5.308  2.129  2.087  0.891  1.002
## [17201]  0.902  1.227  1.082  1.313  1.097  1.763  2.243  1.564  0.973  1.658
## [17211]  0.826  1.983  0.842  1.984  0.826  2.127  1.273  1.710  1.633  1.804
## [17221]  2.633  2.665  0.762  0.761  1.393  0.761  1.243  1.392  2.179  2.992
## [17231]  3.018  2.211  1.843  5.192  1.953  3.112  1.884  2.699  1.334  1.273
## [17241]  9.000  8.350  2.638  3.236  2.681  4.854  3.304  1.740  3.152  2.870
## [17251]  1.371  1.574  1.450  0.999  1.465  1.634  1.351  1.290  1.263  1.020
## [17261]  1.028  2.058  7.328  0.762  0.762  0.762  0.762  0.761  0.762  0.762
## [17271]  0.762  0.761  0.762  0.762  0.762  0.761  0.762  0.762  0.762  0.761
## [17281]  0.762  0.762  0.762  0.761  0.762  0.762  0.762  0.761  0.762  0.762
## [17291]  0.761  0.762  0.762  0.762  0.761  0.762  0.762  0.762  0.761  0.762
## [17301]  0.762  0.762  0.761  0.762  0.762  0.762  0.761  0.762  0.762  0.762
## [17311]  0.761  0.762  0.762  0.762  0.761  0.762  0.762  0.762  0.761  0.762
## [17321]  0.762  0.762  0.761  0.762  0.762  0.762  0.761  0.762  0.762  0.762
## [17331]  0.761  0.762  0.762  0.762  1.358  0.762  0.762  0.762  0.761  0.762
## [17341]  0.762  0.762  1.540  0.762  0.762  0.762  1.228  0.762  0.762  0.762
## [17351]  1.345  0.762  0.762  0.762  0.761  0.762  0.762  0.762  0.761  0.762
## [17361]  0.762  0.762  1.361  0.762  0.762  0.762  0.761  0.762  0.762  0.762
## [17371]  1.501  0.762  0.762  1.110  2.193  0.762  0.762  1.397  1.163  0.761
## [17381]  0.762  2.184  1.383  0.762  0.762  2.098  2.215  0.761  1.035  0.762
## [17391]  1.308  0.762  1.438  2.111  1.716  1.639  0.761  1.471  1.485  0.761
## [17401]  1.376  1.920  0.762  0.761  0.762  0.762  1.481  0.762  1.520  0.762
## [17411]  0.762  2.273  0.762  0.762  0.762  0.762  1.292  0.762  1.439  0.762
## [17421]  1.473  1.153  0.762  0.762  1.202  0.858  0.762  0.762  1.095  0.762
## [17431]  0.762  0.762  1.462  0.762  0.762  0.762  1.390  0.762  0.762  0.762
## [17441]  1.525  0.762  0.762  0.762  1.941  0.762  0.762  0.762  0.916  0.762
## [17451]  0.762  0.762  1.017  0.762  0.762  0.762  1.453  0.762  1.471  0.762
## [17461]  0.761  0.762  1.445  1.458  1.230  0.762  1.278  0.762  0.761  0.762
## [17471]  0.762  0.762  0.761  0.762  0.762  0.761  1.517  0.761  0.761  0.762
## [17481]  0.761  0.761  0.762  1.516  0.761  1.211  0.762  1.367  0.762  0.761
## [17491]  1.100  1.512  0.761  1.392  0.761  0.761  0.957  0.762  0.761  1.528
## [17501]  1.198  1.171  0.761  0.762  1.285  0.819  0.761  0.762  0.999  1.051
## [17511]  1.527  0.762  1.527  0.762  0.761  1.311  0.761  0.762  0.761  0.761
## [17521]  1.078  1.062  0.761  0.761  1.363  1.524  0.761  0.761  1.229  1.522
## [17531]  0.761  1.524  0.761  0.824  1.329  1.510  0.761  0.761  0.762  0.762
## [17541]  1.431  0.761  0.762  0.762  1.636  1.491  0.762  1.507  0.762  1.017
## [17551]  1.094  0.761  0.762  1.115  0.762  1.267  1.516  1.944  1.505  0.762
## [17561]  0.762  1.517  0.761  0.762  0.762  1.524  0.761  1.268  0.762  0.762
## [17571]  0.761  0.762  0.762  0.762  0.761  1.513  0.762  1.523  1.446  0.762
## [17581]  0.762  2.131  0.761  0.762  0.762  1.500  0.762  0.762  1.169  0.761
## [17591]  1.090  0.762  1.256  0.904  0.762  1.523  1.517  0.761  0.761  0.761
## [17601]  0.761  0.761  0.761  1.202  0.761  0.761  1.061  1.321  1.393  0.761
## [17611]  0.871  0.761  1.272  1.466  0.761  1.376  2.139  1.287  1.806  0.761
## [17621]  0.761  1.432  2.278  0.761  1.341  2.942  0.762  0.761  1.497  2.283
## [17631]  0.761  1.527  1.028  1.920  1.416  1.985  0.762  0.761  0.761  1.209
## [17641]  1.517  0.761  0.761  1.131  0.762  0.761  0.761  0.761  0.761  0.761
## [17651]  0.762  1.517  1.509  0.761  0.761  0.762  0.761  1.518  1.082  0.761
## [17661]  0.762  1.751  0.762  0.761  0.762  0.761  0.762  0.761  0.761  0.762
## [17671]  1.324  0.762  1.835  0.762  0.762  0.762  0.762  0.762  1.483  0.762
## [17681]  0.761  0.762  0.761  0.762  1.469  0.762  1.517  0.762  1.163  0.949
## [17691]  0.761  0.888  1.483  0.762  0.762  0.762  1.476  0.762  1.606  0.762
## [17701]  1.663  1.528  0.761  1.114  1.754  0.762  1.527  0.936  2.255  0.761
## [17711]  1.987  0.761  3.135  1.144  1.931  1.272  0.761  0.762  1.339  0.762
## [17721]  0.886  1.487  2.576  1.425  1.902  1.395  2.239  0.762  0.761  2.182
## [17731]  1.434  1.950  2.313  0.762  1.411  2.120  0.761  1.802  1.528  0.762
## [17741]  0.761  0.761  1.238  0.761  0.761  0.761  0.761  0.761  0.761  0.762
## [17751]  1.351  0.761  0.762  0.762  0.762  0.761  0.762  1.414  0.761  0.762
## [17761]  1.518  0.761  0.761  0.762  0.762  0.761  0.761  0.761  0.762  0.761
## [17771]  0.761  1.172  0.762  0.761  0.761  0.761  0.762  0.761  0.761  0.762
## [17781]  1.242  0.761  1.285  1.774  0.762  0.761  0.761  1.154  0.762  0.761
## [17791]  0.761  0.762  0.762  1.526  0.761  0.762  0.762  1.466  0.761  1.095
## [17801]  0.762  0.761  1.345  0.762  0.761  1.756  0.761  0.762  0.762  1.193
## [17811]  1.864  0.762  1.501  1.071  0.761  0.958  1.438  1.370  1.734  0.762
## [17821]  0.762  1.526  2.419  0.762  0.762  1.509  2.256  0.762  0.761  0.762
## [17831]  1.313  1.311  0.762  0.762  1.828  0.761  0.762  1.527  0.978  1.396
## [17841]  0.762  1.489  2.019  1.552  0.762  1.895  1.807  0.762  2.140  1.518
## [17851]  1.500  0.976  0.761  0.762  0.762  0.761  1.506  0.761  1.846  0.761
## [17861]  0.761  1.499  1.781  0.761  2.106  0.761  0.761  0.906  1.953  1.524
## [17871]  0.761  1.492  1.086  0.761  0.761  1.834  0.761  2.236  2.171  0.761
## [17881]  0.761  1.325  2.256  1.327  1.913  0.761  0.761  1.506  0.761  0.761
## [17891]  1.447  1.850  0.761  0.761  1.458  3.027  0.762  1.485  2.218  1.419
## [17901]  1.249  2.290  2.032  0.761  1.527  2.181  0.761  1.479  1.522  0.762
## [17911]  0.761  0.761  1.878  1.896  0.761  1.051  1.198  1.484  0.761  0.761
## [17921]  0.761  0.761  0.762  0.761  0.762  0.762  0.761  0.762  0.761  0.761
## [17931]  0.762  0.761  0.762  0.762  0.761  0.762  1.510  0.761  0.762  0.761
## [17941]  0.761  0.762  1.410  1.468  0.761  0.762  1.041  0.762  0.761  0.762
## [17951]  0.761  0.762  0.761  0.762  1.198  0.762  0.761  0.762  0.761  0.762
## [17961]  0.761  0.762  1.374  0.762  0.761  0.762  0.761  0.762  0.761  1.426
## [17971]  0.761  1.077  0.761  0.761  1.424  0.761  0.761  1.522  0.902  0.762
## [17981]  0.762  0.761  2.384  1.389  0.762  0.761  0.762  0.762  1.190  0.761
## [17991]  0.762  0.762  0.762  1.810  0.762  1.180  0.762  1.088  1.429  0.761
## [18001]  0.762  0.761  1.526  1.405  0.762  1.524  1.232  0.762  1.602  1.523
## [18011]  1.374  0.762  2.134  0.762  0.761  0.762  0.762  0.762  0.761  1.402
## [18021]  0.762  0.762  1.457  0.761  1.160  2.279  0.762  1.519  1.941  0.905
## [18031]  0.895  1.424  1.518  1.286  0.762  1.505  1.532  2.400  2.278  1.476
## [18041]  0.762  0.761  0.762  1.527  1.325  1.988  1.392  0.761  2.274  0.761
## [18051]  1.343  0.761  2.705  0.761  1.944  1.516  1.047  2.351  1.463  1.176
## [18061]  1.321  0.761  0.845  1.522  1.449  0.761  1.523  1.467  1.425  1.475
## [18071]  1.123  2.342  1.510  1.373  1.958  0.761  1.438  1.311  1.017  1.528
## [18081]  0.945  1.511  1.886  1.411  1.367  0.762  1.028  0.761  0.762  1.437
## [18091]  2.794  1.341  0.762  0.762  0.761  1.260  0.762  0.762  2.263  0.761
## [18101]  0.761  1.496  1.492  0.761  1.374  1.726  0.762  0.761  0.761  0.762
## [18111]  0.761  1.350  0.761  0.761  1.521  0.761  1.517  0.761  0.762  0.761
## [18121]  0.762  0.761  1.284  0.761  0.761  0.933  0.761  0.761  1.180  0.761
## [18131]  0.761  0.762  0.761  0.761  1.065  0.761  0.761  1.496  1.411  0.761
## [18141]  0.761  0.826  0.761  0.761  1.097  0.761  0.761  0.761  0.762  0.761
## [18151]  0.761  0.761  0.762  0.761  0.761  1.278  0.762  0.761  0.761  1.260
## [18161]  0.762  0.761  0.761  1.507  0.762  0.761  0.761  1.517  0.762  0.761
## [18171]  0.761  1.104  0.762  0.761  0.761  0.762  0.761  0.761  0.910  0.762
## [18181]  0.761  0.761  0.761  1.446  1.465  0.761  1.407  0.762  0.761  1.763
## [18191]  0.761  0.762  0.761  1.680  2.169  0.762  0.761  0.762  2.056  1.411
## [18201]  0.761  0.762  1.637  0.761  0.761  1.527  0.761  0.761  0.761  1.322
## [18211]  2.267  0.761  0.761  1.401  0.762  0.761  0.761  0.761  0.762  0.761
## [18221]  0.761  0.761  1.171  0.761  0.761  1.387  0.762  0.761  0.761  1.667
## [18231]  0.762  0.761  0.761  1.507  1.315  0.761  0.761  1.104  0.761  0.761
## [18241]  0.761  1.228  1.111  0.761  0.761  1.198  1.795  0.761  0.761  1.278
## [18251]  0.761  0.761  0.761  2.176  0.761  0.761  0.761  0.761  0.761  0.761
## [18261]  0.761  0.761  0.761  1.938  0.761  0.761  0.761  1.039  0.761  0.761
## [18271]  0.761  0.761  1.526  0.761  0.761  1.929  1.475  0.761  0.761  1.268
## [18281]  0.761  0.761  0.761  1.404  0.761  0.761  0.761  1.299  0.761  0.761
## [18291]  0.761  1.368  2.170  0.761  1.380  1.080  2.852  0.761  1.396  2.239
## [18301]  0.761  0.761  1.744  2.031  0.761  0.761  2.272  0.761  0.761  1.372
## [18311]  1.375  0.761  0.761  1.269  1.138  0.761  0.761  0.761  2.199  0.761
## [18321]  0.761  1.522  0.761  0.761  1.497  0.761  1.216  0.761  1.521  1.509
## [18331]  0.761  1.506  0.762  1.419  0.761  0.762  0.887  1.032  0.762  1.894
## [18341]  0.762  0.762  1.526  1.176  0.762  1.475  0.761  0.762  0.761  0.761
## [18351]  0.762  0.761  0.761  0.762  0.761  0.761  0.762  0.762  0.963  1.507
## [18361]  0.762  0.762  0.762  0.762  0.762  1.810  0.762  1.248  0.762  1.397
## [18371]  0.762  0.761  0.762  0.762  1.518  0.762  0.762  1.368  0.762  0.762
## [18381]  1.045  0.761  0.762  0.762  0.761  0.761  0.762  0.762  0.761  1.497
## [18391]  0.762  0.762  1.183  0.762  1.518  0.762  2.116  0.762  0.761  1.527
## [18401]  0.829  0.762  0.761  1.520  1.176  0.762  0.761  0.762  0.762  1.425
## [18411]  0.761  0.762  1.268  0.761  1.386  0.762  1.379  1.315  0.762  0.762
## [18421]  0.761  0.762  1.497  1.129  0.761  0.762  1.447  1.465  1.231  0.761
## [18431]  1.261  0.761  2.411  0.762  0.762  2.157  0.761  1.170  0.762  0.762
## [18441]  0.761  0.762  0.851  0.762  1.528  1.231  1.508  1.668  0.761  1.974
## [18451]  1.527  1.458  0.761  1.343  0.762  0.761  1.028  0.761  0.762  0.762
## [18461]  1.342  1.179  0.761  0.762  1.510  0.762  0.761  0.761  0.762  0.762
## [18471]  1.260  0.762  0.762  1.193  0.900  0.762  0.762  1.412  0.762  0.762
## [18481]  0.762  1.427  0.762  1.517  0.762  0.762  0.762  2.290  0.762  0.762
## [18491]  1.447  0.762  0.762  1.366  0.761  1.217  1.517  1.764  1.252  1.217
## [18501]  1.411  1.525  0.761  0.761  1.523  0.762  0.761  0.761  1.527  1.517
## [18511]  0.761  0.762  0.761  0.762  0.762  0.761  0.761  1.197  1.033  1.032
## [18521]  1.311  0.857  0.882  0.774  0.761  1.383  0.761  1.411  1.325  0.946
## [18531]  0.761  0.850  0.761  0.761  0.880  0.761  0.761  0.946  0.869  0.761
## [18541]  0.761  1.392  1.107  1.042  0.761  1.996  0.899  0.925  0.761  2.256
## [18551]  0.838  0.761  0.761  2.155  0.883  0.761  0.761  1.230  0.761  0.761
## [18561]  0.761  1.404  0.761  0.761  0.762  0.761  1.526  0.761  0.762  0.761
## [18571]  0.762  0.761  1.032  1.593  0.762  0.762  0.762  1.225  0.761  1.248
## [18581]  0.762  0.761  0.857  1.522  1.205  1.009  0.761  1.013  1.527  0.761
## [18591]  0.761  1.394  0.761  1.042  1.802  2.115  0.761  0.761  0.761  0.762
## [18601]  0.761  0.761  0.761  0.762  1.395  0.761  0.762  0.761  0.761  0.761
## [18611]  0.762  1.459  0.761  0.978  1.042  1.527  0.957  0.761  0.762  0.761
## [18621]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [18631]  0.761  0.761  1.524  0.761  0.761  0.761  0.761  1.211  1.343  0.761
## [18641]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.822  0.761
## [18651]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [18661]  1.163  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [18671]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [18681]  0.761  0.761  0.761  0.761  0.761  0.761  1.646  0.761  1.103  0.761
## [18691]  0.761  0.761  0.761  0.762  0.761  0.761  0.761  0.761  0.761  0.761
## [18701]  1.197  0.761  0.761  0.761  0.761  0.761  0.762  0.761  0.761  0.761
## [18711]  0.761  0.761  0.761  0.761  0.761  0.762  0.761  0.762  0.761  0.761
## [18721]  0.762  0.761  0.761  0.761  0.762  0.761  0.761  0.761  0.762  0.761
## [18731]  0.761  0.761  0.762  0.761  0.761  0.761  0.762  0.761  0.761  0.761
## [18741]  0.762  0.761  0.761  0.762  0.761  0.761  0.762  0.761  0.761  0.761
## [18751]  0.762  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [18761]  0.761  0.761  0.761  0.762  0.761  0.761  0.761  0.762  0.761  0.761
## [18771]  0.761  0.987  0.761  0.761  0.761  0.762  0.761  0.761  0.761  0.922
## [18781]  0.761  0.761  0.761  2.120  0.761  0.761  0.761  0.762  0.761  0.761
## [18791]  0.762  0.761  0.761  0.762  0.761  1.399  0.762  0.761  0.761  0.762
## [18801]  0.761  0.761  0.762  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [18811]  0.761  0.761  0.761  0.761  0.761  0.761  1.524  0.761  0.761  0.761
## [18821]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [18831]  0.761  0.761  1.812  0.761  0.761  1.446  0.761  0.761  1.445  0.761
## [18841]  0.761  2.194  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [18851]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.762  0.761
## [18861]  0.761  1.489  0.761  0.761  0.761  0.761  0.761  0.761  0.762  0.761
## [18871]  0.761  0.761  0.761  0.761  0.761  0.762  0.762  0.761  0.761  0.762
## [18881]  0.762  0.761  0.761  0.762  0.762  0.761  0.761  0.762  0.762  0.761
## [18891]  0.761  0.762  0.762  0.761  0.761  0.762  0.762  0.761  0.761  0.762
## [18901]  1.434  0.761  0.761  0.762  0.761  0.761  0.761  0.762  0.761  0.761
## [18911]  0.761  0.762  0.761  0.761  0.761  0.762  0.761  0.761  0.761  0.762
## [18921]  0.761  0.761  0.761  0.762  0.761  0.761  0.761  1.371  1.528  0.761
## [18931]  0.761  0.762  0.762  0.761  0.761  0.762  0.762  0.761  0.761  0.762
## [18941]  1.210  0.761  0.761  1.382  0.761  0.761  0.761  0.761  0.761  0.761
## [18951]  0.761  0.761  0.761  0.761  0.761  1.483  0.761  0.761  0.761  0.762
## [18961]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [18971]  0.761  0.762  0.761  0.761  0.761  0.762  0.761  0.761  0.761  0.762
## [18981]  0.761  0.761  0.761  0.762  0.761  1.164  0.761  0.762  0.761  1.026
## [18991]  0.761  0.762  0.761  0.761  0.761  0.761  0.761  1.046  0.761  0.761
## [19001]  0.761  1.528  0.761  0.761  0.761  0.761  0.761  1.518  0.761  0.761
## [19011]  0.761  0.761  0.761  0.761  1.190  0.761  0.761  0.761  0.761  1.341
## [19021]  0.762  0.761  0.761  0.761  0.761  1.528  0.761  1.448  0.853  0.761
## [19031]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [19041]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [19051]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.904  0.761  0.761
## [19061]  0.761  1.341  0.761  0.761  0.761  1.503  0.761  0.761  0.762  0.762
## [19071]  0.761  0.761  0.762  0.762  0.761  0.761  0.761  0.762  0.761  0.761
## [19081]  0.762  0.762  0.761  0.761  0.761  0.762  0.761  0.762  0.761  0.761
## [19091]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.762  0.761  0.761
## [19101]  0.762  0.762  0.761  0.761  0.762  0.761  0.761  0.762  0.761  0.761
## [19111]  0.762  0.761  0.761  0.761  0.976  0.761  0.761  0.761  0.920  0.761
## [19121]  0.761  0.761  0.761  0.761  1.053  0.761  0.761  2.232  1.521  0.761
## [19131]  1.767  1.519  0.761  1.862  1.392  0.761  0.762  2.180  1.508  0.761
## [19141]  1.301  1.520  0.761  2.582  0.761  0.761  1.522  0.761  0.761  1.519
## [19151]  1.505  0.761  0.761  1.769  0.761  1.523  1.470  1.516  0.761  1.474
## [19161]  0.761  1.448  0.761  1.181  2.145  0.761  0.761  1.970  0.761  0.762
## [19171]  1.467  2.186  1.491  1.517  1.386  1.528  0.761  1.507  1.522  0.761
## [19181]  0.761  1.517  1.517  0.761  2.167  1.847  0.761  0.761  0.761  0.761
## [19191]  0.761  0.761  0.761  0.761  1.520  0.761  0.761  0.761  0.761  0.761
## [19201]  0.761  1.386  0.761  0.761  1.260  0.761  1.389  0.890  0.761  1.527
## [19211]  1.157  0.761  0.864  0.762  1.117  0.761  0.761  0.899  0.761  0.761
## [19221]  0.762  1.709  0.761  0.761  0.762  0.762  0.761  0.761  1.269  0.761
## [19231]  0.761  0.761  1.426  0.761  0.761  0.762  0.762  0.848  0.761  0.762
## [19241]  1.655  0.761  0.761  1.020  1.357  0.761  0.761  0.761  1.335  0.761
## [19251]  1.506  0.761  1.758  0.761  1.467  0.761  1.040  1.486  1.293  0.761
## [19261]  0.904  0.762  0.762  1.411  0.761  0.762  1.241  0.762  1.683  0.762
## [19271]  0.761  0.761  1.106  0.897  1.492  1.523  1.370  0.761  1.431  0.761
## [19281]  1.170  0.761  0.762  0.761  1.520  0.761  1.424  0.761  1.484  1.524
## [19291]  0.761  0.761  1.517  0.762  1.062  1.528  0.762  0.762  1.525  1.509
## [19301]  2.275  1.526  1.500  1.410  0.761  0.761  0.762  0.761  1.232  0.762
## [19311]  1.380  0.761  0.761  1.183  1.505  0.761  0.761  1.140  0.761  1.504
## [19321]  1.972  1.519  1.497  0.761  0.761  1.358  0.761  1.444  1.343  0.761
## [19331]  1.474  1.510  1.473  0.761  0.762  1.037  1.266  0.761  1.470  0.761
## [19341]  1.417  0.761  1.050  0.761  0.762  0.761  0.848  0.761  0.762  0.761
## [19351]  1.130  0.761  1.373  0.761  1.230  1.505  1.373  1.509  1.417  1.528
## [19361]  0.762  1.523  1.516  1.268  1.420  1.430  1.984  0.762  0.762  1.383
## [19371]  1.524  1.341  1.626  0.761  1.527  1.410  1.763  1.462  1.467  0.762
## [19381]  0.762  1.424  1.446  0.761  1.468  1.482  0.762  0.761  0.761  1.290
## [19391]  0.762  1.474  0.761  0.762  1.412  0.761  0.761  0.762  0.761  0.762
## [19401]  0.761  0.762  0.761  1.462  0.761  0.761  0.761  1.451  0.761  0.762
## [19411]  0.761  1.410  1.290  0.761  0.762  0.761  0.762  0.761  0.762  0.761
## [19421]  0.762  0.761  0.762  0.761  0.762  0.761  0.762  1.032  0.762  0.762
## [19431]  0.761  2.075  0.762  0.762  0.762  0.761  0.761  0.762  0.762  0.762
## [19441]  0.762  0.762  0.762  0.761  0.761  1.525  1.503  0.761  0.761  1.322
## [19451]  1.362  1.306  1.553  1.423  0.761  1.516  0.761  0.819  0.761  0.762
## [19461]  0.850  0.762  0.761  0.762  0.761  0.762  0.761  0.762  0.761  0.762
## [19471]  0.761  0.762  0.761  0.762  0.761  0.762  0.761  0.762  0.761  0.762
## [19481]  0.761  0.762  0.762  0.761  0.762  0.761  0.762  0.761  0.761  0.761
## [19491]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.762  0.762  0.761
## [19501]  0.761  0.762  1.197  0.761  0.761  0.762  0.762  0.761  0.761  0.761
## [19511]  0.762  0.761  0.761  0.761  0.762  0.761  0.761  0.761  0.762  0.761
## [19521]  0.761  0.761  0.762  0.761  0.761  0.761  0.762  0.761  0.761  0.761
## [19531]  0.762  0.761  1.523  0.761  1.485  0.761  1.522  0.761  0.761  0.761
## [19541]  0.761  0.761  0.761  1.049  0.761  0.761  0.827  1.374  1.479  0.762
## [19551]  1.634  0.761  1.351  1.512  0.761  1.392  1.311  1.232  1.941  1.506
## [19561]  1.351  1.490  1.900  0.761  0.762  1.527  1.508  1.510  0.762  0.761
## [19571]  1.474  1.341  0.888  0.761  0.762  1.374  0.762  0.761  0.762  0.761
## [19581]  1.523  0.761  0.762  1.475  1.446  0.761  0.762  0.761  0.826  0.761
## [19591]  1.170  0.762  0.762  0.761  2.459  1.526  0.761  1.965  0.761  0.761
## [19601]  0.761  1.452  0.761  1.516  0.761  0.761  1.411  0.761  0.761  0.761
## [19611]  0.761  0.761  1.164  0.761  0.761  1.497  0.761  1.524  0.976  0.761
## [19621]  0.761  1.394  1.496  0.761  0.761  1.860  0.761  0.761  0.761  1.247
## [19631]  0.761  1.814  1.482  0.761  0.761  0.762  1.442  1.412  0.761  0.762
## [19641]  0.761  1.517  0.762  0.762  0.761  1.237  2.142  1.439  0.762  0.761
## [19651]  0.761  1.151  0.761  1.457  0.761  0.762  0.762  0.762  0.761  0.761
## [19661]  0.762  1.133  0.761  0.761  0.762  1.009  0.761  0.761  1.093  0.761
## [19671]  0.761  0.853  0.761  0.761  0.762  1.181  0.761  0.762  0.762  0.761
## [19681]  0.761  0.762  0.762  0.761  0.761  0.762  0.762  0.761  0.761  0.762
## [19691]  0.762  0.761  0.761  0.761  0.762  0.761  0.761  0.761  0.762  0.761
## [19701]  0.761  0.761  0.762  0.761  0.761  0.761  0.762  0.761  0.761  0.761
## [19711]  0.762  0.761  0.761  0.761  0.762  1.042  0.761  0.761  0.762  0.761
## [19721]  0.761  0.761  0.761  0.761  0.761  0.761  0.762  0.761  0.761  0.761
## [19731]  0.762  0.761  0.761  0.762  0.762  1.462  0.761  0.761  0.762  0.761
## [19741]  1.351  0.761  0.761  0.761  0.905  0.761  0.761  0.761  0.761  1.487
## [19751]  0.761  1.464  0.761  0.761  1.217  1.341  1.513  1.527  1.479  0.761
## [19761]  1.230  0.762  1.293  1.362  1.315  1.411  0.761  0.762  1.383  1.398
## [19771]  1.090  1.400  1.186  0.761  1.454  0.761  1.353  1.474  0.761  1.059
## [19781]  1.374  0.761  0.761  0.761  1.411  0.761  0.761  0.761  0.761  0.761
## [19791]  0.761  0.761  1.502  0.761  0.761  1.464  0.761  0.761  0.761  0.761
## [19801]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  1.411
## [19811]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [19821]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.762
## [19831]  0.761  0.761  0.761  0.761  0.761  0.762  0.761  0.761  0.762  0.762
## [19841]  0.761  0.761  0.762  0.761  0.761  0.762  0.762  0.761  0.761  0.762
## [19851]  0.761  0.761  0.761  0.762  0.761  0.761  1.497  0.762  0.761  0.761
## [19861]  1.374  0.761  0.761  1.993  0.761  0.761  0.762  0.900  0.761  0.761
## [19871]  0.762  1.419  0.761  0.761  0.762  0.762  0.761  0.761  0.762  0.762
## [19881]  0.761  0.761  1.118  0.762  0.761  0.761  0.762  0.762  0.761  0.761
## [19891]  0.762  0.762  0.761  0.761  0.762  0.762  0.761  0.761  0.762  0.762
## [19901]  0.761  0.761  0.762  0.762  0.761  0.761  0.762  0.762  0.761  0.761
## [19911]  0.762  1.258  0.761  0.761  1.504  1.429  0.761  0.761  1.511  0.761
## [19921]  1.476  0.761  0.762  1.390  0.762  0.761  0.761  1.133  1.316  0.761
## [19931]  0.888  0.848  0.761  1.257  0.762  1.117  1.334  0.762  0.762  0.762
## [19941]  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762
## [19951]  1.380  1.217  0.762  0.762  0.762  0.762  0.762  0.762  0.762  1.629
## [19961]  0.761  0.762  1.272  0.761  0.762  0.762  1.003  0.808  0.762  0.762
## [19971]  0.761  0.762  0.762  0.762  1.526  0.761  0.762  0.762  0.762  0.761
## [19981]  0.762  0.762  1.411  0.762  0.761  0.762  0.762  0.761  0.762  0.762
## [19991]  0.762  0.762  0.761  0.762  0.762  0.762  0.761  0.762  0.762  0.762
## [20001]  1.216  0.761  0.762  0.762  1.323  0.761  0.762  0.762  1.343  0.762
## [20011]  0.762  0.762  0.761  1.464  1.503  0.761  1.396  0.762  1.225  1.524
## [20021]  1.467  0.762  0.761  1.521  1.411  1.523  0.761  1.790  0.761  1.452
## [20031]  0.762  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.762  0.761
## [20041]  0.761  0.762  0.762  0.761  0.761  0.762  1.343  0.761  0.761  0.762
## [20051]  0.762  0.762  0.762  0.762  0.762  0.762  0.761  0.761  0.761  0.761
## [20061]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [20071]  0.761  0.761  0.761  0.836  0.768  0.868  0.764  0.762  0.762  0.771
## [20081]  0.761  0.762  0.778  0.762  0.762  0.782  0.762  0.778  0.762  0.798
## [20091]  0.762  0.828  0.830  0.816  0.762  0.779  0.762  0.802  0.821  0.761
## [20101]  0.804  0.761  0.824  0.761  0.815  0.761  0.794  0.761  0.782  0.761
## [20111]  0.783  0.761  0.784  0.761  0.787  0.761  0.797  0.761  0.791  0.761
## [20121]  0.798  0.761  0.790  0.761  0.789  0.761  0.767  0.761  0.761  0.761
## [20131]  0.761  0.761  0.761  0.837  0.761  0.767  0.761  0.764  0.761  0.787
## [20141]  0.780  0.782  0.788  0.765  0.779  0.770  0.792  0.773  0.780  0.765
## [20151]  0.777  0.767  0.779  0.765  0.777  0.765  0.778  0.767  0.773  0.766
## [20161]  0.772  0.772  0.774  0.766  0.772  0.771  0.794  0.774  0.778  0.766
## [20171]  0.773  0.762  0.775  0.762  0.778  0.762  0.792  0.795  0.761  0.761
## [20181]  0.761  0.761  0.762  0.761  0.761  0.762  0.761  0.761  0.762  0.761
## [20191]  0.761  0.761  0.761  0.761  0.762  0.762  0.761  0.761  0.762  0.761
## [20201]  0.761  0.761  0.761  0.762  0.761  0.761  0.762  0.761  0.762  0.761
## [20211]  1.032  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [20221]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [20231]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [20241]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [20251]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [20261]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [20271]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [20281]  0.761  0.762  0.771  0.762  0.762  0.762  0.762  0.773  0.762  0.762
## [20291]  0.762  0.762  0.762  0.857  0.762  0.762  0.821  0.762  0.793  0.762
## [20301]  0.766  0.762  0.764  0.761  0.761  0.761  0.763  0.761  0.761  0.761
## [20311]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [20321]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [20331]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [20341]  0.761  0.761  0.761  0.761  0.761  0.761  0.791  0.841  0.762  0.827
## [20351]  0.816  0.762  0.817  0.866  0.762  0.857  0.815  0.762  0.803  0.761
## [20361]  0.762  0.816  0.792  0.762  0.830  0.760  0.762  0.823  0.762  0.816
## [20371]  0.762  0.810  0.762  0.785  0.762  0.781  0.762  0.811  0.761  0.815
## [20381]  0.761  0.810  0.761  0.814  0.761  0.791  0.761  0.786  0.761  0.795
## [20391]  0.761  0.801  0.761  0.789  0.761  0.806  0.761  0.796  0.761  0.788
## [20401]  0.761  0.792  0.761  0.791  0.761  0.798  0.761  0.761  0.761  0.761
## [20411]  0.761  0.761  0.855  0.761  0.799  0.761  0.773  0.761  0.765  0.761
## [20421]  0.788  0.761  0.768  0.761  0.773  0.761  0.799  0.761  0.781  0.804
## [20431]  0.761  0.768  0.761  0.783  0.761  0.804  0.799  0.771  0.761  0.789
## [20441]  0.761  0.793  0.779  0.761  0.775  0.761  0.786  0.761  0.799  0.827
## [20451]  0.761  0.762  0.763  0.762  0.763  0.762  0.762  0.763  0.761  0.762
## [20461]  0.761  0.762  0.762  0.762  0.761  0.762  0.764  0.762  0.762  0.761
## [20471]  0.764  0.762  0.761  0.763  0.762  0.761  0.761  0.762  0.761  0.761
## [20481]  0.762  0.763  0.762  0.762  0.762  0.761  0.762  0.762  0.761  0.761
## [20491]  0.761  0.762  0.761  0.762  0.761  0.761  0.761  0.761  0.761  0.762
## [20501]  0.762  0.761  0.762  0.761  0.761  0.762  0.762  0.761  0.762  0.761
## [20511]  0.761  0.761  0.763  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [20521]  0.761  0.762  0.761  0.761  0.762  0.761  0.761  0.761  0.761  0.781
## [20531]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [20541]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.762  0.768
## [20551]  0.762  0.761  0.761  0.762  0.763  0.761  0.762  0.761  0.761  0.761
## [20561]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [20571]  0.761  0.761  0.762  0.761  0.761  0.761  0.761  0.761  0.761  0.762
## [20581]  0.762  0.761  0.762  0.761  0.761  0.762  0.761  0.761  0.761  0.763
## [20591]  0.761  0.761  0.761  0.761  0.762  0.762  0.762  0.762  0.762  0.762
## [20601]  0.762  0.762  0.762  0.762  0.762  0.762  0.761  0.761  0.761  0.761
## [20611]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [20621]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [20631]  0.762  0.761  0.761  0.762  0.761  0.761  0.762  0.761  0.761  0.762
## [20641]  0.761  0.761  0.762  0.761  0.761  0.762  0.761  0.760  0.762  0.761
## [20651]  0.761  0.762  0.761  0.762  0.761  0.761  0.762  0.761  0.761  0.762
## [20661]  0.762  0.761  0.761  0.761  0.761  0.762  0.761  0.761  0.761  0.761
## [20671]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [20681]  0.761  0.762  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [20691]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [20701]  0.823  0.879  0.934  0.910  0.975  0.874  0.994  1.192  1.321  0.876
## [20711]  1.038  0.953  0.761  0.912  0.761  0.834  0.761  0.817  0.925  0.859
## [20721]  1.156  1.589  1.320  1.063  0.778  0.777  0.777  0.781  0.782  0.786
## [20731]  0.761  1.191  1.054  1.080  1.053  0.963  0.912  0.964  1.086  1.111
## [20741]  0.868  1.135  0.770  1.059  0.879  0.797  0.869  1.038  0.786  0.869
## [20751]  1.038  0.786  1.084  0.905  0.987  1.055  1.109  1.047  0.765  0.761
## [20761]  0.761  0.761  0.761  0.761  0.761  0.762  1.116  1.479  0.890  1.234
## [20771]  1.203  0.839  0.761  0.762  0.761  0.761  0.762  0.762  0.762  0.761
## [20781]  0.761  1.936  1.062  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [20791]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  1.197
## [20801]  1.925  1.316  0.769  0.761  0.761  0.771  0.762  0.761  0.761  0.761
## [20811]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [20821]  0.761  0.761  0.761  0.762  0.762  0.762  0.853  0.762  0.762  0.825
## [20831]  0.831  0.815  0.823  0.792  0.771  0.761  0.761  0.763  0.764  1.960
## [20841]  0.761  0.761  0.761  0.761  1.026  2.246  0.761  0.980  1.027  0.761
## [20851]  0.761  1.022  0.761  0.808  0.882  0.987  0.887  0.802  0.914  0.761
## [20861]  1.041  2.264  1.097  1.104  0.930  2.437  0.932  0.904  1.239  0.761
## [20871]  1.154  0.761  0.764  0.936  0.921  0.762  0.761  0.761  0.761  0.761
## [20881]  0.761  0.761  0.761  0.765  0.765  0.765  0.765  0.765  0.761  0.846
## [20891]  0.845  0.809  0.802  0.801  1.149  1.897  0.761  1.030  1.114  0.870
## [20901]  0.888  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.830
## [20911]  1.195  1.058  0.888  0.792  0.763  0.761  0.761  0.762  0.779  0.761
## [20921]  0.761  0.760  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [20931]  0.762  0.761  1.025  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [20941]  0.761  0.761  0.761  0.765  0.765  0.933  0.828  0.824  0.786  0.761
## [20951]  0.761  0.761  0.761  0.761  0.761  0.905  1.215  2.211  1.042  1.600
## [20961]  2.753  0.761  1.033  0.823  0.761  0.761  1.969  1.072  1.040  0.969
## [20971]  0.890  0.775  0.970  1.309  1.190  0.761  1.310  1.317  0.761  0.968
## [20981]  1.060  0.761  0.761  1.312  1.298  1.196  0.762  0.939  1.206  1.045
## [20991]  1.150  0.971  1.044  1.167  0.782  1.312  1.366  0.761  2.554  1.397
## [21001]  1.087  1.370  1.199  1.383  0.795  1.155  1.400  1.401  1.423  1.226
## [21011]  1.380  1.366  1.397  1.407  1.415  1.420  1.413  1.315  1.093  1.048
## [21021]  1.097  1.073  1.086  1.113  1.075  1.146  1.116  1.089  1.094  1.131
## [21031]  1.131  1.078  1.157  1.166  1.047  1.083  1.089  1.104  1.171  1.175
## [21041]  1.268  1.400  1.095  1.084  1.084  0.927  0.912  1.122  1.114  1.071
## [21051]  1.068  1.069  1.044  1.039  1.032  1.065  0.819  0.879  0.922  0.927
## [21061]  1.054  0.799  0.919  0.937  0.793  0.891  0.800  0.843  0.907  0.882
## [21071]  0.881  0.926  0.856  0.812  0.777  0.790  0.763  0.767  0.788  0.779
## [21081]  0.764  0.761  0.846  0.790  0.795  0.791  0.830  0.761  0.880  0.846
## [21091]  0.927  0.987  0.938  0.835  0.904  0.812  0.873  0.815  0.876  0.837
## [21101]  0.761  0.945  0.978  0.863  0.872  0.866  0.917  0.868  0.936  0.864
## [21111]  0.910  0.948  0.858  0.837  0.922  0.871  0.795  0.845  0.832  0.785
## [21121]  0.798  0.781  0.761  0.766  0.761  0.790  0.761  0.801  0.761  0.781
## [21131]  0.779  0.770  0.784  0.858  0.774  0.761  0.773  0.761  0.842  0.921
## [21141]  0.761  0.794  0.783  0.878  0.761  0.766  0.827  0.777  0.761  0.796
## [21151]  0.761  0.778  0.761  0.761  0.761  0.781  0.761  0.762  0.761  0.782
## [21161]  0.803  0.762  0.779  0.807  0.836  0.783  1.132  0.809  0.773  0.803
## [21171]  0.890  0.791  1.145  0.763  0.989  0.778  0.761  0.761  0.762  0.762
## [21181]  0.761  1.315  1.081  1.091  1.091  1.080  1.073  1.092  1.083  1.067
## [21191]  1.083  1.082  1.012  1.085  1.078  1.081  1.145  1.102  1.085  1.080
## [21201]  1.075  1.059  1.070  1.082  1.068  1.351  1.199  1.230  1.206  1.183
## [21211]  1.088  1.182  1.076  1.037  0.940  0.874  0.829  1.077  1.370  1.101
## [21221]  1.056  1.077  1.055  1.121  1.131  0.828  0.816  1.214  1.122  1.092
## [21231]  1.065  1.073  1.062  1.076  1.075  1.084  0.843  0.765  0.762  0.761
## [21241]  0.761  0.761  0.761  0.761  0.761  0.765  0.762  0.790  0.805  0.761
## [21251]  0.761  0.810  0.761  0.801  0.798  0.802  0.761  0.805  0.761  0.761
## [21261]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [21271]  0.761  0.761  0.797  0.761  0.855  0.874  0.819  0.817  0.762  0.899
## [21281]  0.921  0.762  0.791  0.762  0.763  0.762  0.911  0.764  0.816  0.762
## [21291]  0.783  0.780  0.784  0.838  0.784  0.768  0.791  0.761  0.777  0.761
## [21301]  0.761  0.764  0.767  0.878  0.789  0.807  0.761  0.806  0.821  0.763
## [21311]  0.808  0.791  0.761  0.850  0.761  0.912  0.953  0.857  0.870  0.782
## [21321]  0.794  0.763  0.763  0.765  0.763  0.869  0.998  0.761  1.514  1.166
## [21331]  1.103  0.761  0.991  0.827  0.811  0.968  1.071  0.896  0.791  0.791
## [21341]  0.799  0.799  0.983  0.763  0.763  1.064  1.172  0.762  0.762  1.154
## [21351]  0.762  0.762  1.145  0.762  0.762  1.221  0.762  1.028  0.926  1.094
## [21361]  0.778  1.232  0.987  1.093  1.056  1.280  1.041  0.762  1.124  1.108
## [21371]  0.995  1.179  1.121  1.184  1.142  1.203  0.761  1.214  1.191  0.761
## [21381]  1.347  0.761  0.761  0.761  1.143  0.761  1.071  1.058  1.125  1.011
## [21391]  1.235  1.188  1.022  0.831  1.354  1.041  1.140  1.434  1.333  1.435
## [21401]  0.923  0.762  0.916  0.762  0.925  0.762  0.893  0.762  0.762  0.762
## [21411]  0.879  0.762  0.762  0.762  0.885  0.762  0.762  0.762  0.832  0.762
## [21421]  0.762  0.762  0.761  0.762  0.762  0.761  0.761  0.761  0.762  0.762
## [21431]  0.842  0.763  0.762  0.762  0.761  0.761  0.762  0.762  0.761  0.761
## [21441]  0.762  0.768  0.761  0.761  0.762  0.762  0.761  0.761  0.762  0.868
## [21451]  0.761  0.761  0.762  0.762  0.761  0.761  0.762  0.761  0.761  0.761
## [21461]  0.762  0.761  0.761  0.761  0.762  0.761  0.761  0.761  0.762  0.777
## [21471]  0.761  0.761  0.762  0.761  0.761  0.761  0.762  0.761  0.761  0.761
## [21481]  0.762  0.976  0.761  0.761  0.762  0.824  0.761  0.761  0.762  0.761
## [21491]  0.761  0.761  0.762  0.761  0.761  0.761  0.762  1.050  0.761  0.761
## [21501]  0.762  0.914  0.761  0.761  0.762  0.762  0.761  0.761  0.803  0.762
## [21511]  0.761  0.761  0.762  0.761  0.761  1.030  0.777  0.761  0.761  0.761
## [21521]  0.762  0.761  0.761  0.859  0.761  0.761  0.761  0.925  0.766  0.761
## [21531]  0.761  0.805  0.788  0.952  0.761  0.761  0.761  0.762  0.830  0.761
## [21541]  0.930  0.761  0.762  0.861  0.761  1.046  0.761  0.762  0.838  0.761
## [21551]  1.029  0.761  0.762  0.900  0.761  1.067  0.762  0.762  0.813  0.761
## [21561]  1.032  0.761  0.852  0.761  1.029  0.761  0.860  0.761  0.812  0.762
## [21571]  0.855  0.761  0.826  0.762  0.851  0.762  0.783  0.762  0.762  0.808
## [21581]  0.761  0.761  0.762  0.762  0.767  0.762  0.761  0.762  0.763  0.761
## [21591]  0.767  0.762  0.763  0.791  0.761  0.762  0.787  0.761  0.762  0.761
## [21601]  0.761  0.762  0.786  0.761  0.761  0.762  0.762  0.761  0.761  0.762
## [21611]  0.761  0.761  0.761  0.762  0.760  0.761  0.761  0.762  0.761  0.761
## [21621]  0.761  0.762  0.769  0.761  0.761  0.762  0.761  0.761  0.761  0.762
## [21631]  0.761  0.761  0.761  0.762  0.761  0.761  0.761  0.762  0.762  1.421
## [21641]  0.761  1.258  0.761  0.761  1.062  0.761  0.761  1.560  0.761  0.761
## [21651]  0.883  0.761  0.761  1.490  0.761  0.761  1.740  0.761  0.761  1.594
## [21661]  0.761  0.761  0.914  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [21671]  0.761  0.761  0.761  1.139  1.160  0.761  0.761  1.251  0.761  0.761
## [21681]  1.320  0.761  0.761  1.086  0.873  0.761  0.761  0.761  0.761  1.480
## [21691]  0.761  0.761  1.173  0.761  0.761  0.965  0.856  0.761  1.231  0.839
## [21701]  0.761  1.138  0.761  0.761  1.713  0.761  0.821  1.477  1.220  0.761
## [21711]  1.239  0.761  0.761  1.096  0.761  0.761  0.968  0.761  0.761  1.116
## [21721]  0.761  0.761  0.960  0.761  0.761  0.905  0.761  0.761  0.762  0.761
## [21731]  0.761  0.928  0.761  0.761  0.762  0.815  0.762  0.761  0.761  0.762
## [21741]  0.761  0.761  0.762  0.761  0.762  0.761  0.762  0.763  0.761  1.044
## [21751]  0.762  0.761  1.015  0.762  0.761  1.062  0.761  0.761  0.772  0.927
## [21761]  1.053  1.366  0.761  1.089  0.761  0.761  0.856  0.761  1.385  0.761
## [21771]  0.976  0.761  0.761  0.980  0.761  0.761  0.761  0.761  1.039  0.761
## [21781]  0.761  0.966  0.761  0.761  1.382  0.761  0.761  0.764  0.763  0.769
## [21791]  0.768  0.767  0.763  0.768  0.767  0.763  0.766  0.767  0.768  0.765
## [21801]  0.768  0.769  0.764  0.762  0.764  0.769  0.775  0.777  0.774  0.764
## [21811]  0.763  0.763  0.765  0.776  0.768  0.766  0.766  0.769  0.767  0.771
## [21821]  0.769  0.771  0.774  0.783  0.771  0.777  0.771  0.770  0.773  0.769
## [21831]  0.768  0.771  0.768  0.782  0.769  0.768  0.770  0.771  0.770  0.767
## [21841]  0.933  0.984  0.767  0.766  0.770  0.771  0.767  0.771  0.812  0.772
## [21851]  0.774  0.776  0.772  0.771  0.772  0.772  0.780  0.774  0.772  0.777
## [21861]  0.775  0.779  0.774  0.784  0.784  0.782  0.778  0.790  0.786  0.795
## [21871]  0.804  0.787  0.774  0.766  0.767  0.767  0.768  0.768  0.768  0.766
## [21881]  0.783  0.882  0.942  0.937  0.879  0.880  0.868  0.810  0.885  0.810
## [21891]  0.895  0.858  0.859  0.861  0.841  0.856  0.851  0.802  0.827  0.861
## [21901]  0.878  0.819  0.849  0.837  0.805  0.822  0.804  0.806  0.824  0.856
## [21911]  0.851  0.809  0.801  0.841  4.832  0.814  1.640  3.098  2.500  1.902
## [21921]  1.418  1.149  3.463  3.322  2.462  4.689  2.104  3.951  1.163  1.916
## [21931]  5.188  9.283  5.285  7.480  5.268  4.734  2.401  1.368  6.784  7.548
## [21941]  8.372  7.651  3.549  2.562  3.470  4.713  3.940  4.456  2.341  0.800
## [21951]  1.035  1.765  2.084  0.761  2.041  2.859  1.486  1.236  1.462  2.585
## [21961]  2.550  1.069  1.470  2.396  1.507  2.935  2.560  1.376  2.506  2.977
## [21971]  3.008  2.307  2.109  1.194  1.323  2.921  2.733  1.399  1.146  0.761
## [21981]  0.762  0.762  0.762  0.761  0.773  4.751  0.840  1.325  1.318  3.289
## [21991]  1.365  2.159  1.689  1.339  1.366  1.519  0.761  1.947  0.761  3.015
## [22001]  1.344  1.915  1.304  1.377  2.112  1.964  2.690  2.701  1.327  2.051
## [22011]  2.335  1.637  1.938  1.819  2.169  1.678  2.958  2.479  2.556  2.553
## [22021]  1.994  2.595  2.454  2.907  3.825  3.501  3.662  2.434  1.626  2.087
## [22031]  1.935  1.374  1.299  1.316  1.357  1.370  2.410 11.112  1.002  1.747
## [22041]  0.987  3.289  0.761  0.766  0.769  0.767  0.761  1.641  0.764  0.779
## [22051]  0.761  0.777  0.761  0.799  0.761  0.865  0.761  0.776  0.791  0.812
## [22061]  0.796  0.804  0.769  0.787  0.762  0.778  0.761  0.768  0.761  0.773
## [22071]  0.761  0.761  0.762  0.763  0.761  0.777  0.761  0.772  0.761  0.770
## [22081]  0.762  0.770  0.890  0.767  0.767  0.777  0.789  0.791  0.771  0.776
## [22091]  0.801  0.789  0.761  0.790  0.766  0.770  0.766  1.106  0.763  0.761
## [22101]  0.761  0.761  0.761  0.762  0.761  0.762  0.942  0.808  0.818  0.781
## [22111]  0.774  0.770  0.763  0.762  0.762  3.364  2.305  3.743  1.334  0.866
## [22121]  1.242  2.620  1.318  1.350  4.056 14.143  0.761  0.764  0.761  0.761
## [22131]  0.761  0.762  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [22141]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.762
## [22151]  0.761  0.761  0.763  0.761  0.761  0.761  0.762  0.765  0.763  0.761
## [22161]  0.763  0.762  0.761  0.761  0.761  0.762  0.761  0.761  0.761  0.761
## [22171]  0.762  0.761  0.761  0.762  0.761  0.768  0.761  0.762  0.761  0.762
## [22181]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.831  0.761  0.761
## [22191]  0.763  0.762  0.761  0.762  0.761  0.761  0.761  0.762  0.762  0.761
## [22201]  0.761  0.761  0.762  0.762  0.761  0.761  0.761  0.761  0.761  0.761
## [22211]  0.763  0.761  0.761  0.953  1.248  2.270  0.880  1.280  0.948  0.852
## [22221]  0.866  0.927  1.219  1.140  0.906  1.693  1.397  1.496  0.762  1.798
## [22231]  1.639  1.969  1.273  0.820  0.764  2.286  1.545  1.317  1.652  1.032
## [22241]  1.025  1.609  1.419  1.202  1.556  1.224  1.320  1.372  1.235  1.312
## [22251]  1.385  1.361  1.240  1.220  1.047  1.276  0.938  1.115  0.819  0.974
## [22261]  0.852  0.809  1.353  1.047  1.769  1.037  1.388  1.204  1.385  1.217
## [22271]  1.325  1.480  1.363  1.205  1.214  1.068  1.127  1.119  1.487  1.195
## [22281]  1.326  2.230  1.216  1.947  1.274  1.242  2.661  1.053  1.731  1.006
## [22291]  1.310  0.832  1.312  1.121  1.133  1.061  1.260  3.824  2.356  3.138
## [22301]  1.570  0.761  1.922  1.802  1.903  2.543  2.018  1.419  2.163  1.358
## [22311]  2.507  1.373  3.497  1.185  1.775  1.499  1.695  1.320  1.497  3.223
## [22321]  1.708  1.482  1.584  0.981  1.815  1.280  1.262  1.195  1.629  1.667
## [22331]  1.850  1.311  2.221  1.473  2.281  1.585  1.937  1.903  1.816  2.011
## [22341]  1.570  1.525  1.626  2.030  2.222  2.025  1.451  1.655  1.620  1.811
## [22351]  1.371  1.828  1.625  2.229  1.459  1.804  1.342  1.567  1.767  1.910
## [22361]  1.253  1.631  2.058  2.415  2.496  2.582  2.549  3.221  3.198  2.659
## [22371]  2.393  6.273  0.884  2.560  2.291  1.259  3.591  1.812  1.520  1.245
## [22381]  1.408  1.345  1.548  1.873  2.307  1.937  1.594  1.934  0.768  0.780
## [22391]  0.796  0.771  0.773  0.832  0.775  0.793  0.793  0.818  1.284  1.080
## [22401]  0.853  0.797  0.771  0.812  0.792  0.768  2.646  0.764  0.789  0.769
## [22411]  3.347  0.761  0.761  0.763  0.913  0.761  0.761  0.762  0.878  0.930
## [22421]  0.878  0.904  0.938  0.854  0.895  0.761  0.761  0.901  0.869  0.907
## [22431]  0.944  1.015  1.062  1.211  0.907  1.055  0.927  0.862  0.801  0.772
## [22441]  0.772  1.141  2.052  1.327  1.064  1.251  0.766  0.827  0.761  0.762
## [22451]  0.760  2.665  3.201  2.609  1.484  1.464  2.043  1.729  2.165  2.171
## [22461]  1.141  1.074  1.231  0.968  2.022  2.569  1.199  2.163  1.209  0.986
## [22471]  1.159  2.233  2.049  1.927  1.375  1.560  2.208  1.339  4.605  1.439
## [22481]  1.120  3.304  2.901  1.943  1.630  1.489  2.601  1.295  1.725  1.722
## [22491]  1.785  2.368  1.868  1.323  1.362  1.264  2.747  1.675  1.329  1.632
## [22501]  0.761  0.761  0.761  1.167  1.943  2.052  1.395  0.874  1.154  1.700
## [22511]  2.483  2.226  1.354  0.889  1.167  0.941  1.527  1.585  0.929  1.610
## [22521]  1.404  1.294  1.506  1.701  7.170  1.222  1.182  2.647  3.872  5.113
## [22531]  2.936  4.079  1.434  1.158  0.875  1.524  0.943  1.330  0.919  1.030
## [22541]  2.179  1.704  1.172  1.351  0.960  2.477  1.868  2.635  0.761  0.761
## [22551]  0.761  0.762  0.761  0.761  0.761  0.761  1.257  1.243  1.318  1.124
## [22561]  1.224  0.940  0.762  0.761  1.300  1.407  1.817  1.688  1.554  1.488
## [22571]  0.929  1.016  1.065  1.289  1.486  1.477  1.004  1.960  1.107  0.776
## [22581]  2.145  1.354  1.625  0.762  1.211  1.206  2.212  2.686  1.407  2.038
## [22591]  1.256  1.239  1.133  0.761  1.048  1.677  1.146  0.764  1.274  0.903
## [22601]  1.073  1.060  1.276  0.875  1.372  0.762  1.291  1.245  1.372  1.819
## [22611]  0.990  1.383  1.400  0.933  1.184  1.178  1.230  0.814  1.016  0.803
## [22621]  0.799  0.914  1.059  1.200  1.418  1.663  1.644  1.790  1.085  1.604
## [22631]  1.350  1.638  1.123  1.543  1.185  1.434  1.544  1.416  1.148  2.137
## [22641]  2.010  1.326  2.391  2.464  1.999  1.863  1.625  1.840  2.308  1.849
## [22651]  1.206  1.309  1.041  1.312  0.966  1.739  1.033  1.572  1.662  1.378
## [22661]  0.786  1.042  0.969  1.073  0.895  1.142  1.165  1.464  1.100  0.800
## [22671]  1.437  1.410  0.897  2.171  1.701  1.303  0.863  0.894  1.306  3.566
## [22681]  1.301  0.766  0.842  1.082  1.069  1.273  0.808  1.528  5.018  0.761
## [22691]  0.761  0.762  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [22701]  0.762  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  1.615
## [22711]  2.881  0.761  0.763  0.761  0.761  0.761  0.766  0.763  0.761  0.761
## [22721]  0.761  0.761  0.761  2.006  0.761  0.768  0.791  0.842  1.543  1.873
## [22731]  1.404  1.584  1.869  1.551  1.422  1.890  1.374  0.993  1.864  2.140
## [22741]  1.684  2.259  1.885  2.351  2.108  2.487  1.692  2.155  1.258  2.126
## [22751]  1.061  3.545  3.784  2.508  1.966  1.736  1.164  1.480  1.662  1.645
## [22761]  1.695  1.880  1.833  1.907  2.437  2.673  2.132  1.843  2.303  2.015
## [22771]  2.051  2.386  1.955  1.895  1.676  1.838  1.832  1.681  0.955  2.230
## [22781]  1.637  2.149  2.604  2.135  1.092  2.264  1.188  1.789  1.297  1.564
## [22791]  1.743  1.635  1.800  1.553  1.822  1.583  1.768  1.494  1.423  1.892
## [22801]  1.367  1.560  1.389  1.563  1.863  2.135  1.551  1.852  1.513  1.532
## [22811]  2.282  1.900  1.922  2.072  2.015  1.955  1.938  2.179  2.046  2.070
## [22821]  1.584  1.334  2.151  2.185  1.751  2.105  2.263  2.123  2.071  2.054
## [22831]  2.187  1.757  1.072  1.345  1.302  1.461  1.445  1.095  0.798  1.017
## [22841]  1.726  1.833  1.823  1.990  1.925  1.928  1.651  1.603  1.608  1.625
## [22851]  1.837  1.771  1.332  2.977  1.021  0.927  1.044  1.369  1.007  1.177
## [22861]  1.234  0.892  1.361  1.429  1.055  1.127  1.133  1.084  1.131  0.978
## [22871]  1.299  1.274  1.319  1.237  1.208  1.069  1.348  1.409  2.249  1.384
## [22881]  3.306  1.449  1.281  1.432  1.515  2.323  1.250  1.389  1.768  1.548
## [22891]  0.917  1.024  1.011  1.170  1.015  0.941  1.283  1.140  1.371  1.093
## [22901]  1.157  1.228  1.043  1.128  1.251  1.208  1.021  1.027  1.150  1.174
## [22911]  1.396  1.297  1.617  1.313  2.206  1.021  1.871  1.490  1.829  1.462
## [22921]  2.283  1.496  2.336  1.478  1.640  1.361  1.784  1.408  1.349  1.282
## [22931]  1.301  1.155  1.378  1.468  1.440  1.446  1.454  1.462  1.437  1.416
## [22941]  1.426  1.652  2.027  2.280  2.356  2.029  2.218  2.260  1.471  0.985
## [22951]  2.626  1.424  2.313  3.677  2.748  1.906  2.214  2.158  2.021  1.419
## [22961]  1.933  1.721  1.683  2.127  1.298  1.782  2.056  1.118  1.353  2.055
## [22971]  1.362  1.088  1.401  1.006  1.526  1.196  1.714  1.033  1.628  1.412
## [22981]  1.281  1.004  1.395  2.818  1.305  2.479  1.282  1.795  1.662  2.090
## [22991]  0.761  2.766  0.761  2.030  1.131  1.897  0.892  1.476  2.065  1.403
## [23001]  1.590  2.632  1.992  2.011  1.586  1.307  2.409  1.927  1.580  1.597
## [23011]  1.870  1.495  1.466  1.572  1.510  1.582  1.452  2.296  1.712  1.656
## [23021]  1.657  1.515  1.556  1.375  1.714  1.906  1.842  1.486  1.385  1.459
## [23031]  1.454  1.436  1.907  1.367  2.175  2.606  2.086  1.326  1.848  1.157
## [23041]  1.463  1.100  1.330  0.934  1.264  1.410  1.484  1.266  1.035  1.267
## [23051]  1.167  1.195  1.393  1.343  1.487  1.505  1.433  1.397  1.527  0.761
## [23061]  1.396  0.993  1.395  1.526  1.527  1.183  1.260  1.195  0.762  1.903
## [23071]  1.284  1.306  0.761  1.518  1.785  1.428  1.515  0.761  1.491  1.526
## [23081]  1.912  1.604  2.878  1.515  1.880  1.508  1.884  1.470  1.525  1.514
## [23091]  1.522  1.413  2.170  1.790  1.753  1.593  1.387  1.295  1.527  1.463
## [23101]  0.761  0.761  1.096  1.052  0.761  1.465  1.523  1.504  1.500  1.520
## [23111]  0.761  1.453  0.761  0.762  1.985  2.194  1.414  2.162  0.775  1.708
## [23121]  0.863  0.838  1.123  0.905  1.329  0.986  0.977  0.761  0.761  0.763
## [23131]  1.370  1.438  0.761  0.761  0.762  0.763  1.035  0.761  0.761  1.109
## [23141]  0.761  0.763  0.762  0.761  1.475  0.762  0.762  0.761  1.503  0.762
## [23151]  0.762  0.761  0.762  1.469  1.477  0.761  1.341  1.275  1.982  0.761
## [23161]  1.474  0.762  0.762  0.761  0.762  1.462  0.761  0.762  0.762  1.104
## [23171]  0.761  0.762  1.387  1.456  1.244  0.762  0.762  1.337  0.762  0.762
## [23181]  0.762  1.995  0.762  0.762  1.192  1.442  0.762  0.762  0.761  0.762
## [23191]  0.762  0.762  0.761  0.762  1.186  0.762  0.761  1.103  1.528  0.762
## [23201]  0.761  1.470  0.762  0.762  0.761  0.762  0.762  0.762  0.923  1.355
## [23211]  0.762  0.762  0.761  0.762  0.762  0.762  0.761  0.762  0.762  0.762
## [23221]  0.761  0.762  0.762  0.762  0.761  0.762  0.998  0.762  0.761  0.762
## [23231]  0.762  0.762  0.761  0.762  0.762  0.762  0.761  0.762  0.762  0.762
## [23241]  0.761  0.762  0.762  0.762  0.761  0.762  0.762  0.762  0.761  0.762
## [23251]  0.762  0.762  0.761  0.762  0.762  0.762  0.761  0.762  0.762  0.762
## [23261]  0.761  1.424  0.762  0.761  0.762  0.762  2.050  0.761  0.762  0.762
## [23271]  0.762  1.424  0.762  0.762  2.282  0.762  0.762  0.762  1.264  0.762
## [23281]  0.762  0.761  0.762  0.762  0.762  0.761  0.762  0.762  0.762  0.761
## [23291]  0.762  0.762  0.761  0.762  0.762  0.762  1.347  0.762  0.762  0.762
## [23301]  0.761  0.762  0.762  0.762  0.761  0.762  0.762  0.762  0.761  0.762
## [23311]  0.762  0.762  1.438  0.762  0.762  0.762  1.153  0.762  0.762  0.762
## [23321]  0.761  0.762  0.762  0.761  0.761  0.762  1.180  0.762  0.761  0.762
## [23331]  0.762  0.762  0.761  0.762  0.762  0.762  0.761  0.762  0.762  0.762
## [23341]  0.761  1.260  0.762  0.762  0.761  0.762  0.762  0.761  0.762  0.761
## [23351]  0.762  0.762  0.761  0.762  0.762  0.761  0.762  0.762  0.761  0.762
## [23361]  0.761  0.762  0.761  0.761  0.761  0.761  0.761  0.762  0.761  0.761
## [23371]  0.762  0.761  0.761  0.761  0.762  0.761  0.761  0.761  0.761  0.761
## [23381]  0.762  1.119  0.762  0.762  1.374  0.762  1.146  1.089  1.924  0.762
## [23391]  0.762  2.134  0.762  0.762  0.762  0.762  0.892  0.762  0.762  0.762
## [23401]  0.762  0.764  0.762  0.762  0.762  0.762  1.189  0.761  0.761  1.603
## [23411]  0.762  0.762  2.100  1.518  2.212  1.527  1.506  1.177  1.410  0.761
## [23421]  0.762  0.762  0.762  0.762  1.524  1.418  2.244  0.762  0.762  0.762
## [23431]  0.762  0.761  1.686  1.632  0.762  2.230  1.526  0.761  1.746  1.527
## [23441]  2.283  1.162  1.322  1.343  1.327  1.036  0.761  0.762  1.336  0.771
## [23451]  2.161  1.964  1.768  1.289  1.553  0.762  0.762  1.178  1.438  0.761
## [23461]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.897  1.419
## [23471]  0.761  0.761  1.521  1.397  0.761  0.761  1.140  1.449  0.762  0.761
## [23481]  1.505  1.467  0.761  1.250  1.527  0.761  0.762  1.515  1.180  0.762
## [23491]  0.762  1.511  1.267  0.762  0.762  1.419  1.514  0.762  0.761  1.490
## [23501]  1.517  0.762  0.762  1.073  1.039  0.762  0.762  0.896  0.762  0.762
## [23511]  0.762  0.761  0.899  0.761  0.762  0.823  0.761  0.761  0.762  1.321
## [23521]  0.890  0.761  0.761  1.170  1.491  0.761  0.761  0.761  1.513  0.761
## [23531]  0.762  0.761  1.517  0.762  0.762  1.095  1.524  0.761  0.762  1.520
## [23541]  1.386  0.761  0.762  1.514  1.527  0.761  0.762  1.501  1.488  0.761
## [23551]  0.762  1.526  1.453  0.761  1.411  1.099  0.761  0.761  1.657  1.453
## [23561]  0.761  0.761  1.507  0.874  0.761  0.761  1.287  1.117  0.761  0.761
## [23571]  1.463  1.090  0.761  0.761  1.893  1.472  0.761  0.761  2.134  1.525
## [23581]  0.761  0.761  0.761  1.482  0.761  0.761  1.524  1.863  0.761  0.761
## [23591]  1.527  1.293  0.761  0.761  1.298  1.517  0.761  0.761  1.305  1.318
## [23601]  0.761  0.761  1.453  0.761  0.761  1.511  0.761  0.761  0.761  0.761
## [23611]  0.761  0.761  0.761  1.431  0.761  0.761  0.761  0.761  0.761  1.936
## [23621]  0.761  0.761  0.761  1.480  0.761  1.524  0.762  0.761  0.761  1.346
## [23631]  1.137  0.761  0.761  1.475  0.761  0.761  1.092  0.761  1.482  0.869
## [23641]  0.761  0.761  0.861  0.761  0.763  0.761  0.761  1.740  0.761  0.761
## [23651]  1.192  0.842  1.322  0.868  0.761  0.761  1.109  0.858  0.761  0.761
## [23661]  1.159  1.375  0.762  0.761  0.761  0.761  0.762  0.761  0.762  0.761
## [23671]  0.762  0.761  0.762  0.762  0.762  0.762  1.103  0.762  0.762  0.762
## [23681]  0.762  0.761  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.761
## [23691]  0.762  0.761  0.761  0.761  0.777  0.761  0.762  0.978  0.762  0.762
## [23701]  0.761  1.100  0.762  0.761  0.761  0.761  0.762  1.198  0.761  0.762
## [23711]  0.761  0.762  0.938  0.762  0.761  0.761  0.761  0.762  0.761  0.761
## [23721]  1.507  0.761  0.761  1.333  0.762  0.761  0.761  0.761  0.762  0.761
## [23731]  0.761  0.761  0.762  0.761  1.453  1.357  0.761  0.762  0.761  0.761
## [23741]  0.761  0.761  0.761  0.761  0.761  0.761  1.527  0.761  0.761  0.761
## [23751]  0.761  0.761  0.761  1.431  0.761  0.761  1.936  0.761  0.761  1.527
## [23761]  1.528  0.761  0.761  0.761  0.761  0.761  0.761  1.507  1.411  0.761
## [23771]  0.761  1.523  1.619  1.511  0.761  0.762  0.761  0.761  0.761  0.761
## [23781]  0.761  1.247  0.761  0.761  0.889  0.761  0.761  0.761  0.761  0.761
## [23791]  0.761  0.761  0.761  0.880  0.761  0.761  0.761  0.761  0.761  0.761
## [23801]  0.761  1.503  0.761  1.503  1.375  1.619  1.274  0.761  1.504  1.477
## [23811]  0.987  0.761  0.761  0.762  1.655  0.761  0.761  0.762  0.762  1.487
## [23821]  0.761  0.762  0.761  0.762  1.180  1.405  0.761  0.761  0.762  1.483
## [23831]  1.416  0.761  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762
## [23841]  0.762  0.762  0.762  0.762  0.762  0.761  0.762  0.762  0.762  0.762
## [23851]  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762
## [23861]  0.762  1.207  0.762  0.762  0.762  0.762  1.147  0.761  0.762  0.762
## [23871]  1.360  0.761  0.761  0.762  0.762  0.762  0.761  0.761  1.374  0.761
## [23881]  2.198  0.761  0.761  0.762  0.761  0.761  0.761  0.762  1.311  1.509
## [23891]  1.341  0.762  1.410  1.112  0.761  0.761  0.762  0.761  0.761  0.761
## [23901]  1.414  1.526  0.761  0.762  0.762  0.762  0.895  1.381  0.762  0.762
## [23911]  0.762  1.518  0.762  0.762  0.762  1.358  0.762  0.762  0.762  0.762
## [23921]  1.459  0.762  0.762  0.762  1.424  1.445  0.762  0.762  0.762  0.762
## [23931]  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762  1.137
## [23941]  0.762  0.762  0.762  1.431  1.132  0.762  0.762  0.762  2.070  0.762
## [23951]  0.762  0.762  1.511  0.762  0.762  0.762  0.762  0.762  0.762  0.762
## [23961]  0.762  1.311  1.805  0.762  0.762  1.410  0.761  0.762  0.762  0.762
## [23971]  2.981  0.762  0.762  0.762  1.523  0.762  0.762  0.762  0.762  0.762
## [23981]  0.762  0.762  0.761  1.311  0.762  0.762  0.762  1.796  0.761  0.762
## [23991]  0.762  0.762  0.762  1.142  1.317  0.762  0.762  0.762  0.761  1.407
## [24001]  0.762  0.762  0.762  0.762  0.761  1.527  0.762  0.762  0.762  0.762
## [24011]  0.762  1.481  0.762  0.762  0.762  0.762  1.310  0.762  0.829  0.762
## [24021]  0.762  1.499  0.762  0.762  0.762  0.762  1.457  0.762  0.762  0.762
## [24031]  1.438  0.762  0.762  0.762  0.762  0.761  0.761  0.762  0.762  0.762
## [24041]  0.762  0.761  0.761  1.284  0.762  0.762  0.761  0.761  0.762  0.762
## [24051]  0.762  0.761  1.424  0.762  0.761  0.761  0.762  0.762  0.761  0.762
## [24061]  0.762  0.761  1.221  1.167  0.761  1.527  0.762  1.475  0.761  1.012
## [24071]  0.762  0.762  0.762  0.762  0.761  1.502  0.762  0.762  0.987  0.762
## [24081]  0.761  1.517  0.762  0.761  0.762  0.762  0.761  0.762  0.762  0.762
## [24091]  0.761  0.762  0.761  0.762  0.761  1.063  0.761  0.762  0.761  0.762
## [24101]  0.761  0.761  0.761  0.761  0.761  0.761  0.762  0.762  0.762  0.851
## [24111]  0.762  0.761  0.996  0.761  0.762  0.761  0.762  1.949  0.761  0.762
## [24121]  0.761  0.910  0.761  0.762  0.761  0.761  0.761  0.761  0.761  0.761
## [24131]  1.109  0.761  1.422  0.761  0.828  0.761  0.761  0.761  0.761  0.761
## [24141]  0.761  0.761  0.761  0.761  1.496  0.761  0.761  1.084  0.761  0.761
## [24151]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [24161]  1.123  0.761  0.761  0.839  0.761  1.287  0.762  1.252  1.025  1.507
## [24171]  0.761  0.761  1.438  1.151  1.411  1.263  1.109  0.761  0.762  0.761
## [24181]  0.762  0.761  0.761  0.762  0.761  0.762  0.762  0.762  0.761  0.762
## [24191]  0.762  0.762  0.762  0.761  0.762  0.762  0.761  0.761  0.761  0.761
## [24201]  0.762  0.761  0.761  0.761  0.761  0.762  0.762  0.762  0.762  0.761
## [24211]  0.762  0.762  0.762  0.761  0.762  0.762  0.761  0.762  1.197  0.762
## [24221]  0.761  0.761  1.444  0.761  0.761  1.374  0.761  0.762  0.761  0.762
## [24231]  0.761  0.761  0.762  0.761  0.762  0.761  0.762  0.761  0.761  0.762
## [24241]  0.761  0.762  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [24251]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [24261]  2.122  1.527  0.761  1.137  0.761  0.761  1.517  0.761  0.762  0.761
## [24271]  0.761  1.411  0.761  0.761  0.761  1.923  0.761  2.027  0.762  0.761
## [24281]  0.762  0.762  0.762  0.762  0.762  0.762  1.184  0.762  0.761  0.761
## [24291]  0.762  1.973  0.762  0.762  0.762  0.762  0.762  0.762  0.762  0.762
## [24301]  0.762  0.761  0.762  0.761  0.762  0.761  0.761  0.761  0.761  0.761
## [24311]  0.761  0.762  0.761  0.762  0.761  0.761  0.762  1.522  0.761  0.762
## [24321]  0.762  0.761  0.762  0.761  0.762  0.761  0.761  0.761  0.761  0.762
## [24331]  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761  0.761
## [24341]  0.762  0.762  0.761  1.487  0.761  1.374  1.224  1.092  1.063  1.492
## [24351]  3.191  3.424  3.158  1.598  3.116  3.439  2.335  2.040  1.572  1.234
## [24361]  0.859  0.951  1.050  1.334  1.842  1.980  1.396  1.422  1.710  0.800
## [24371]  1.660  1.581  0.912  1.470  2.270  1.712  1.388  1.674  1.374  1.622
## [24381]  3.220  1.287  2.252  2.905  2.041  2.809  3.249  0.761  3.040  0.762
## [24391]  2.005  0.761  2.242  6.419  0.764  1.384  0.981  2.480  3.608  2.917
## [24401]  7.595  2.294  4.346  2.149  2.994  2.159  5.007  1.702  4.290  5.729
## [24411]  6.758  3.881  4.552  2.657  3.757  7.285  4.808  3.424  4.868  2.899
## [24421]  3.431  3.986  6.578  2.155  2.754  1.850  2.488  2.237  2.340  2.265
## [24431]  2.243  2.335  2.407  1.828  2.474  1.890  2.221  1.607  2.110  1.351
## [24441]  2.284  2.179  2.415  2.181  2.111  2.252  1.791  1.779  2.011  1.826
## [24451]  2.093  2.073  1.781  2.205  1.675  2.050  1.803  3.258  1.579  2.664
## [24461]  2.077  2.387  1.452  1.894  1.742  4.319  3.300  1.027  1.600  1.146
## [24471]  1.763  1.148  2.872  1.146  4.088  2.790  3.351  1.686  1.925  1.258
## [24481]  1.442  4.061  1.273  9.722  6.436  7.040  7.093  6.753  7.974 11.149
## [24491]  5.494  4.417  7.775  3.139  7.882  6.483  5.957  6.503  3.114  1.377
## [24501]  1.038  1.676  2.082  1.662  2.318  1.089  2.262  2.070  2.683  2.112
## [24511]  1.988  2.609  2.168  2.096  2.234  2.241  2.334  1.862  2.165  1.979
## [24521]  1.317  1.999  1.466  1.311  2.323  1.401  2.208  1.303  2.038  1.395
## [24531]  2.108  1.231  1.986  1.103  1.304  1.551  1.393  1.862  2.244  1.681
## [24541]  1.100  0.761  0.762  0.761  0.762  3.032  1.247  3.194  5.279  4.739
## [24551]  2.027  4.129  2.561  3.269  4.189  1.468  1.269  2.681  1.993  2.791
## [24561]  3.215  4.315  5.551  5.477  4.364  2.829  3.659  3.144  8.605  2.874
## [24571]  1.698  1.654  1.384  1.006  1.268  2.364  2.821  1.674  1.478  1.579
## [24581]  2.116  1.766  1.583  1.634  1.715  1.687  1.610  1.042  0.761  0.762
## [24591]  0.761  0.762  0.761  0.762  1.697  7.618  1.218  4.648  8.875  7.401
## [24601]  7.650  3.922  5.944  1.640  1.295  1.684  1.587  1.788  1.459  2.072
## [24611]  2.242  2.465  1.566  2.078  1.872  2.071  1.988  1.831  2.185  0.762
## [24621]  0.761  0.764  0.761  0.761  0.761  0.761  0.761  0.761  0.761  1.571
## [24631]  0.767  0.763  0.761  0.762  0.762  2.166  1.864  1.422  1.613  1.715
## [24641]  1.022  1.887  2.045  2.290  2.097  2.167  1.959  2.725  2.360  1.607
## [24651]  0.975  1.316  1.182  1.247  2.683  1.432  7.266  1.521  2.602  1.035
## [24661]  2.552  1.350  9.255  3.083  6.517  5.857  4.672  8.100  1.992  2.044
## [24671]  4.335  2.551  0.953  1.719  1.327  1.498  0.958  1.054  3.069  1.220
## [24681]  1.707  3.020  2.032  2.029  1.286  0.958  1.488  1.145  1.303  1.423
## [24691]  1.258  1.280  1.226  0.787  1.091  1.541 11.975  3.568  2.704  2.313
## [24701]  9.652  2.231  4.648  5.858  6.964  4.225  3.395  1.601  1.447  2.763
## [24711]  2.698  1.701  1.150  0.761  0.762  0.761  0.762  0.906  0.762  0.942
## [24721]  1.081  1.572  5.374  1.648  1.684  1.385  2.130  4.621  3.821  5.233
## [24731]  6.187  6.514  4.849  4.905  3.598  5.273  5.458  4.593  6.036  1.658
## [24741]  2.021  1.518  1.432  1.406  1.489  1.780  1.826  1.373  1.685  1.751
## [24751]  1.870  1.888  2.182  1.909  1.654  1.909  1.512  1.752  1.773  1.219
## [24761]  1.786  1.599  1.624  1.348  1.242  2.105  1.723  1.160  1.434  2.827
## [24771]  1.432  2.746  1.380  1.306  2.255  1.131  0.761  0.762  0.761  0.762
## [24781]  0.761  0.762  0.765  1.163  3.480  1.520  5.813  8.022  6.582  5.747
## [24791]  6.456  6.995  6.768  7.067  6.423  4.862  5.624  5.636  6.363  7.289
## [24801]  3.677  2.475  1.455  2.521  1.344  1.324  1.387  1.302  1.457  2.289
## [24811]  1.684  1.723  1.390  1.416  1.844  2.075  1.967  2.161  1.554  1.750
## [24821]  1.868  1.428  1.504  1.294  1.524  1.291  1.726  1.265  1.920  1.779
## [24831]  2.614  1.390  1.731  1.947  1.655  2.101  1.453 10.786  6.310  6.884
## [24841]  5.578  6.710  7.002  3.200  3.147  5.500  2.516  3.878  4.383  6.587
## [24851]  7.341  2.817  5.978  1.048  3.365  1.382  2.060  1.324  1.615  1.994
## [24861]  1.396  1.870  1.801  1.352  1.654  3.879  2.038  1.388  1.798  1.292
## [24871]  2.265  1.424  1.171  1.646  1.462  1.501  1.555  1.374  1.388  0.916
## [24881]  0.910  0.775  0.761  0.761  0.761  0.760  0.761  0.761  0.761  1.652
## [24891]  0.761  2.099  2.149  1.954  2.059  1.973  2.024  1.862  0.889  0.762
## [24901]  0.761  0.764  0.785  0.798  0.762  0.761  0.767  0.761  0.826  0.816
## [24911]  0.834  1.380  0.977  1.327  1.476  1.716  1.336  1.398  1.331  1.694
## [24921]  1.141  1.885  2.088  1.295  2.055  1.922  2.003  1.917  6.133  1.294
## [24931]  1.869  1.772  1.944  1.858  1.910  1.993  1.758  1.492  1.906  1.147
## [24941]  0.761  0.762  0.772  0.761  0.766  0.763  0.765  0.765  0.761  0.761
## [24951]  0.761  0.761  0.761  0.803  0.786  0.773  0.761  0.948  0.922  0.963
## [24961]  1.288  1.767  1.270  1.740  1.307  1.366  1.152  1.126  1.161  1.057
## [24971]  1.038  1.264  1.332  1.146  1.314  1.235  1.328  1.295  1.548  1.313
## [24981]  1.135  1.161  1.397  1.236  1.477  1.443  0.761  0.761  0.761  0.761
## [24991]  0.761  0.762  0.761  0.766  0.766  0.778  0.931  1.466  1.276  1.274
## [25001]  1.649  1.594  1.919  1.885  1.948  1.161  2.038  2.014  2.018  2.083
## [25011]  1.405  0.761  0.762  0.766  0.839  0.779  0.898  0.769  0.787  0.844
## [25021]  0.800  0.766  0.762  0.830  0.880  0.764  0.761  0.762  0.763  0.766
## [25031]  0.779  0.764  0.762  0.761  0.871  0.816  0.761  0.764  0.761  0.763
## [25041]  0.870  1.034  0.783  0.870  1.738  0.763  1.222  0.763  0.876  0.763
## [25051]  0.858  0.763  1.000  0.763  0.867  0.765  0.763  1.116  0.763  1.153
## [25061]  0.763  0.789  0.853  0.782  0.776  0.949  0.851  0.761  0.768  0.761
## [25071]  0.764  0.762  0.763  0.762  0.763  0.761  0.763  0.762  0.763  0.762
## [25081]  0.763  0.762  0.763  0.761  0.763  0.761  0.763  0.761  0.761  0.761
## [25091]  0.761  0.761  0.777  0.767  0.761  0.766  0.761  0.761  0.761  0.761
## [25101]  0.761  0.761  0.762  0.761  0.766  0.764  0.766  0.799  1.112  1.257
## [25111]  1.141  0.761  0.761  0.762  0.761  0.761  0.761  0.762  0.761  0.763
## [25121]  0.761  0.763  0.761  0.763  0.761  0.761  0.763  0.761  0.766  0.761
## [25131]  0.763  1.220  2.004  1.548  1.125  1.856  1.601  1.807  1.193  1.292
## [25141]  2.059  4.520  1.784  1.503  1.435  1.235  2.067  1.776  1.297  0.951
## [25151]  2.109  1.969  1.828  1.311  1.951  1.410  1.397  1.793  1.091  1.057
## [25161]  1.252  1.531  1.493  1.539  1.527  1.523  1.519  1.430  1.450  0.780
## [25171]  1.102  0.830  0.802  0.846  1.018  0.910  0.907  0.899  0.913  0.931
## [25181]  1.329  0.861  0.903  1.127  1.282  1.281  1.155  0.762  0.762  0.763
## [25191]  0.881  0.767  0.882  0.772  0.892  0.768  0.889  0.767  0.902  0.763
## [25201]  0.883  0.767  0.813  0.761  0.761  0.762  0.769  0.768  0.806  0.913
## [25211]  0.822  0.775  0.816  0.824  0.837  0.881  0.924  1.102  0.894  0.912
## [25221]  0.840  0.860  0.919  0.771  1.062  1.196  1.039  0.826  0.848  0.878
## [25231]  1.327  1.366  1.151  1.319  1.406  1.348  1.430  1.292  1.454  1.052
## [25241]  1.425  1.023  1.512  1.481  2.090  0.826  1.181  1.188  1.230  1.249
## [25251]  1.361  1.194  1.623  1.430  1.818  1.365  1.492  1.675  1.711  1.637
## [25261]  1.556  1.361  1.329  1.498  1.503  1.474  0.914  1.001  0.885  0.889
## [25271]  0.960  0.945  0.963  0.941  0.902  0.977  0.950  0.965  0.919  1.162
## [25281]  1.189  1.016  0.945  0.990  1.327  2.822  1.159  1.013  1.321  1.952
## [25291]  1.868  0.896  1.783  0.935  0.880  1.060  1.139  0.931  0.903  0.887
## [25301]  0.895  0.851  0.835  0.837  0.858  0.948  0.836  0.824  0.916  0.886
## [25311]  0.949  0.941  0.894  0.831  0.901  0.891  0.836  0.929  0.908  1.014
## [25321]  0.773  0.990  0.843  0.924  0.873  0.891  0.906  0.928  0.837  0.937
## [25331]  0.881  0.951  0.902  0.903  1.236  1.169  1.061  0.828  1.264  1.268
## [25341]  1.246  1.118  0.972  1.051  0.865  1.235  1.215  1.250  1.283  1.148
## [25351]  1.138  1.182  1.187  1.231  1.119  0.768  1.173  0.761  1.163  0.761
## [25361]  1.199  0.795  0.761  0.793  0.761  0.779  0.761  0.763  0.762  0.777
## [25371]  0.761  0.762  0.764  0.776  0.761  0.761  0.761  0.761  0.761  0.761
## [25381]  0.827  0.761  0.761  1.283  0.761  0.974  0.764  0.761  0.761  1.097
## [25391]  0.761  0.762  0.761  0.761  0.840  0.840  0.775  0.837  0.767  0.879
## [25401]  0.804  0.878  1.055  0.933  1.108  2.027  0.865  0.918  1.092  0.902
## [25411]  0.873  0.842  0.774  0.771  0.826  0.765  0.777  0.796  0.785  0.799
## [25421]  0.784  0.845  0.768  0.860  0.780  0.861  0.788  0.869  0.866  0.845
## [25431]  0.908  0.944  0.904  0.888  0.821  0.880  0.906  0.819  0.869  0.872
## [25441]  0.844  0.932  0.891  0.784  0.794  0.804  0.840  0.920  1.010  0.980
## [25451]  0.811  0.766  0.888  0.762  0.931  0.877  0.871  0.776  0.871  0.853
## [25461]  1.032  0.945  0.864  1.012  0.929  0.932  0.900  0.761  0.761  0.761
## [25471]  0.761  1.841  1.842  1.411  1.700  1.652  2.004  1.569  1.360  0.974
## [25481]  1.600  1.983  1.928  0.871  1.367  1.417  1.443  1.640  1.518  0.838
## [25491]  0.764  0.769  0.887  0.773  0.785  0.773  0.775  0.768  1.490  0.765
## [25501]  0.770  0.762  0.786  0.764  0.782  0.761  0.773  0.763  0.761  0.763
## [25511]  0.798  1.706  0.762  0.785  0.765  0.778  0.814  0.889  0.816  0.761
## [25521]  0.802  1.105  0.761  0.762  1.388  1.882  0.763  0.766  0.769  1.566
## [25531]  0.761  0.762  0.763  0.762  0.762  0.761  0.761  0.762  0.761  0.761
## [25541]  0.761  0.761  0.763  0.761  0.764  0.761  0.762  0.837  0.774  1.688
## [25551]  1.370  1.729  1.936  2.181  1.330  1.000  0.762  0.763  0.762  0.762
## [25561]  0.761  0.762  0.761  0.761  0.763  0.762  0.762  1.239  0.833  1.252
## [25571]  0.761  0.761  1.274  0.901  0.779  0.771  0.769  0.779  0.782  0.791
## [25581]  0.834  0.829  0.818  0.810  0.942  0.825  0.828  0.860  0.981  1.026
## [25591]  0.872  0.861  0.846  0.765  1.463  0.764  0.763  0.763  0.766  0.763
## [25601]  1.435  0.884  0.831  0.817  0.848  0.789  0.779  0.775  0.787  0.891
## [25611]  0.787  0.794
getFitted(Final8)  #predictions of the model for all points
x = getSimulations(Final8, nsim = 5, type = "refit")  #extract simulations from the model
getRefit(Final8, x[[1]])  #model with simulated data
## Formula:          
## KUD95 ~ Habitat + ComImport + MonitArea_km2 + (1 | Transmitter) +  
##     (1 | Species)
## Data: newData
##       AIC       BIC    logLik  df.resid 
## 17812.211 17885.568 -8897.105     25603 
## Random-effects (co)variances:
## 
## Conditional model:
##  Groups      Name        Std.Dev.
##  Transmitter (Intercept) 0.2797  
##  Species     (Intercept) 0.1554  
## 
## Number of obs: 25612 / Conditional model: Transmitter, 850; Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0756 
## 
## Fixed Effects:
## 
## Conditional model:
##            (Intercept)         Habitatdemersal  Habitatpelagic-neritic  
##               -0.06283                -0.01202                 0.32698  
##        ComImportmedium          ComImportminor           MonitArea_km2  
##               -0.11378                -0.27259                 0.02942
getRefit(Final8, getObservedResponse(Final8))  #model with real data
## Formula:          
## KUD95 ~ Habitat + ComImport + MonitArea_km2 + (1 | Transmitter) +  
##     (1 | Species)
## Data: newData
##       AIC       BIC    logLik  df.resid 
##  9939.556 10012.913 -4960.778     25603 
## Random-effects (co)variances:
## 
## Conditional model:
##  Groups      Name        Std.Dev.
##  Transmitter (Intercept) 0.2895  
##  Species     (Intercept) 0.1486  
## 
## Number of obs: 25612 / Conditional model: Transmitter, 850; Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0753 
## 
## Fixed Effects:
## 
## Conditional model:
##            (Intercept)         Habitatdemersal  Habitatpelagic-neritic  
##               -0.07381                -0.07891                 0.49851  
##        ComImportmedium          ComImportminor           MonitArea_km2  
##               -0.14387                -0.11094                 0.02942
#create a dataframe with the simulated data and the true data
df <- data.frame(x$sim_1, x$sim_2, x$sim_3, week_kuds$KUD95, week_kuds$Habitat, week_kuds$ComImport)

#plot KUD95 (real and simulated) against Commercial Importance
grid.arrange(ggplot(data = df, aes(x = week_kuds.ComImport, y=week_kuds.KUD95)) +
  geom_boxplot(fill = "gray") + scale_y_continuous(limits = c(0, 15)) + xlab("Commercial Importance") + ylab("real KUD95"), ggplot(data = df, aes(x = week_kuds.ComImport, y=x.sim_1)) +
  geom_boxplot(fill = "lightblue4") + scale_y_continuous(limits = c(0, 15)) + xlab("Commercial Importance") + ylab("KUD95 simulation 1"), ggplot(data = df, aes(x = week_kuds.ComImport, y=x.sim_2)) +
  geom_boxplot(fill = "deepskyblue3") + scale_y_continuous(limits = c(0, 15)) + xlab("Commercial Importance") + ylab("KUD95 simulation 2"), ggplot(data = df, aes(x = week_kuds.ComImport, y=x.sim_3)) + geom_boxplot(fill = "deepskyblue4") + scale_y_continuous(limits = c(0, 15)) + xlab("Commercial Importance") + ylab("KUD95 simulation 3"), ncol = 4)

#plot KUD95 (real and simulated) against Habitat
grid.arrange(ggplot(data = df, aes(x = week_kuds.Habitat, y=week_kuds.KUD95)) +
  geom_boxplot(fill = "gray") + scale_y_continuous(limits = c(0, 15)) + xlab("Habitat") + ylab("real KUD95"), ggplot(data = df, aes(x = week_kuds.Habitat, y=x.sim_1)) +
  geom_boxplot(fill = "lightblue4") + scale_y_continuous(limits = c(0, 15)) + xlab("Habitat") + ylab("KUD95 simulation 1"), ggplot(data = df, aes(x = week_kuds.Habitat, y=x.sim_2)) +
  geom_boxplot(fill = "deepskyblue3") + scale_y_continuous(limits = c(0, 15)) + xlab("Habitat") + ylab("KUD95 simulation 2"), ggplot(data = df, aes(x = week_kuds.Habitat, y=x.sim_3)) + geom_boxplot(fill = "deepskyblue4") + scale_y_continuous(limits = c(0, 15)) + xlab("Habitat") + ylab("KUD95 simulation 3"), ncol = 4)

The normality and homoscedasticity assumptions aren’t met. However, given the large dataset, this violation may not be a big problem. To investigate this, we simulated the response values and compared with the real ones. After observing the results, we concluded that the patterns were similar and the model can be said to be well adjusted.

Modelling KUD50

We tested the full linear model and checked the assumptions.

#Full model assumption checking
lmtotal1 <- lm(KUD50 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + ReceiverDensity + MonitArea_km2, data = week_kuds)
summary(lmtotal1)
## 
## Call:
## lm(formula = KUD50 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + 
##     Troph + Habitat + Migration + ComImport + ReceiverDensity + 
##     MonitArea_km2, data = week_kuds)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.23144 -0.05035 -0.02695  0.03196  2.96212 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             8.340e-02  1.129e-02   7.390 1.51e-13 ***
## LengthStd               7.539e-02  9.203e-03   8.192 2.68e-16 ***
## BodyMassStd            -6.505e-02  7.941e-03  -8.191 2.71e-16 ***
## Longevity              -1.222e-03  6.938e-05 -17.618  < 2e-16 ***
## Vulnerability           4.369e-04  1.255e-04   3.483 0.000497 ***
## Troph                   1.245e-02  2.778e-03   4.483 7.39e-06 ***
## Habitatdemersal        -1.728e-02  3.274e-03  -5.279 1.31e-07 ***
## Habitatpelagic-neritic  3.889e-02  3.619e-03  10.746  < 2e-16 ***
## Migrationoceanodromous  2.340e-02  3.235e-03   7.235 4.77e-13 ***
## ComImportmedium        -3.367e-02  2.240e-03 -15.029  < 2e-16 ***
## ComImportminor          1.511e-03  8.762e-03   0.172 0.863122    
## ReceiverDensity         6.079e-04  4.427e-05  13.733  < 2e-16 ***
## MonitArea_km2           9.996e-03  2.007e-04  49.813  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1185 on 25599 degrees of freedom
## Multiple R-squared:  0.1693, Adjusted R-squared:  0.1689 
## F-statistic: 434.6 on 12 and 25599 DF,  p-value: < 2.2e-16
#Normality assumption
qqnorm(lmtotal1$residuals)
qqline(lmtotal1$residuals, col = 2)

#Homocedascity assumption
plot(lmtotal1$fitted.values, lmtotal1$residuals)
abline(h=0, col="red")

As expected, the linear model do not fit well to our data, since the assumptions are not met.

We tried to fit different types of models. We modeled the response against each predictor variable separately. We then compare the AIC of each model to see which one fitted better.

#Modeling (each variable separately)

#Length Standardised
lm_length1 <- lm(KUD50 ~ LengthStd, data=week_kuds)
glm_length1 <- glm(KUD50 ~ LengthStd, data=week_kuds, family=Gamma(link="log"))
gam_length1 <- gam(KUD50 ~ LengthStd, data=week_kuds, family=Gamma(link="log"))
glmmF_length1 <- glmmTMB(KUD50 ~ LengthStd + (1|File), data=week_kuds, family=Gamma(link="log"))
glmmT_length1 <- glmmTMB(KUD50 ~ LengthStd + (1|Transmitter), data=week_kuds, family=Gamma(link="log")) #control = glmerControl(optimizer = "bobyqa")
glmmS_length1 <- glmmTMB(KUD50 ~ LengthStd + (1|Species), data=week_kuds, family=Gamma(link="log"))
gammF_length1 <- gamm(KUD50 ~ s(LengthStd), random=list(File=~1), data= week_kuds, family=Gamma(link="log"))   #Preference for gmcv package since gamm4 uses glm methods
## 
##  Maximum number of PQL iterations:  20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
#gamm4F_length1 <- gamm4(KUD50 ~ s(LengthStd), random = ~(1|File), data = week_kuds, family=Gamma(link="log"))
gammT_length1 <- gamm(KUD50 ~ s(LengthStd), random=list(Transmitter=~1), data= week_kuds, family=Gamma(link="log"))   #Preference for gmcv package since gamm4 uses glm methods
## 
##  Maximum number of PQL iterations:  20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
#gamm4T_length1 <- gamm4(KUD50 ~ s(LengthStd), random = ~(1|Transmitter), data = week_kuds, family=Gamma(link="log"))
gammS_length1 <- gamm(KUD50 ~ s(LengthStd), random=list(Species=~1), data= week_kuds, family=Gamma(link="log"))   #Preference for gmcv package since gamm4 uses glm methods
## 
##  Maximum number of PQL iterations:  20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
#gamm4S_length1 <- gamm4(KUD50 ~ s(LengthStd), random = ~(1|Species), data = week_kuds, family=Gamma(link="log"))

AIC(lm_length1, glm_length1, gam_length1, glmmF_length1, glmmT_length1, glmmS_length1)
##               df       AIC
## lm_length1     3 -31828.99
## glm_length1    3 -52604.94
## gam_length1    3 -47252.02
## glmmF_length1  4 -65932.14
## glmmT_length1  4 -75923.01
## glmmS_length1  4 -61214.33
summary(gammF_length1$lme)
## Linear mixed-effects model fit by maximum likelihood
##   Data: data 
##        AIC      BIC    logLik
##   22567.53 22608.28 -11278.76
## 
## Random effects:
##  Formula: ~Xr - 1 | g
##  Structure: pdIdnot
##              Xr1      Xr2      Xr3      Xr4      Xr5      Xr6      Xr7      Xr8
## StdDev: 1.325791 1.325791 1.325791 1.325791 1.325791 1.325791 1.325791 1.325791
## 
##  Formula: ~1 | File %in% g
##         (Intercept)  Residual
## StdDev:    0.325026 0.3737981
## 
## Variance function:
##  Structure: fixed weights
##  Formula: ~invwt 
## Fixed effects:  list(fixed) 
##                       Value  Std.Error    DF    t-value p-value
## X(Intercept)     -1.4855690 0.04762374 25563 -31.193875       0
## Xs(LengthStd)Fx1 -0.2212537 0.04684247 25563  -4.723356       0
##  Correlation: 
##                  X(Int)
## Xs(LengthStd)Fx1 -0.001
## 
## Standardized Within-Group Residuals:
##        Min         Q1        Med         Q3        Max 
## -1.7982212 -0.4745062 -0.2449103  0.1407538 23.7885183 
## 
## Number of Observations: 25612
## Number of Groups: 
##           g File %in% g 
##           1          48
summary(gammT_length1$lme)
## Linear mixed-effects model fit by maximum likelihood
##   Data: data 
##        AIC      BIC    logLik
##   10269.63 10310.39 -5129.817
## 
## Random effects:
##  Formula: ~Xr - 1 | g
##  Structure: pdIdnot
##                  Xr1          Xr2          Xr3          Xr4          Xr5
## StdDev: 0.0002300292 0.0002300292 0.0002300292 0.0002300292 0.0002300292
##                  Xr6          Xr7          Xr8
## StdDev: 0.0002300292 0.0002300292 0.0002300292
## 
##  Formula: ~1 | Transmitter %in% g
##         (Intercept)  Residual
## StdDev:   0.3330887 0.2805653
## 
## Variance function:
##  Structure: fixed weights
##  Formula: ~invwt 
## Fixed effects:  list(fixed) 
##                       Value  Std.Error    DF    t-value p-value
## X(Intercept)     -1.4839857 0.01196298 24761 -124.04815  0.0000
## Xs(LengthStd)Fx1  0.0170314 0.01153395 24761    1.47663  0.1398
##  Correlation: 
##                  X(Int)
## Xs(LengthStd)Fx1 0.082 
## 
## Standardized Within-Group Residuals:
##         Min          Q1         Med          Q3         Max 
## -2.56415741 -0.39930461 -0.11072128  0.08036937 16.90436482 
## 
## Number of Observations: 25612
## Number of Groups: 
##                  g Transmitter %in% g 
##                  1                850
summary(gammS_length1$lme)
## Linear mixed-effects model fit by maximum likelihood
##   Data: data 
##        AIC      BIC    logLik
##   29667.58 29708.33 -14828.79
## 
## Random effects:
##  Formula: ~Xr - 1 | g
##  Structure: pdIdnot
##              Xr1      Xr2      Xr3      Xr4      Xr5      Xr6      Xr7      Xr8
## StdDev: 1.677282 1.677282 1.677282 1.677282 1.677282 1.677282 1.677282 1.677282
## 
##  Formula: ~1 | Species %in% g
##         (Intercept) Residual
## StdDev:   0.3382615 0.430117
## 
## Variance function:
##  Structure: fixed weights
##  Formula: ~invwt 
## Fixed effects:  list(fixed) 
##                       Value  Std.Error    DF    t-value p-value
## X(Intercept)     -1.4517870 0.06289454 25581 -23.082877  0.0000
## Xs(LengthStd)Fx1 -0.1271003 0.05214480 25581  -2.437448  0.0148
##  Correlation: 
##                  X(Int)
## Xs(LengthStd)Fx1 0     
## 
## Standardized Within-Group Residuals:
##        Min         Q1        Med         Q3        Max 
## -1.5599808 -0.4844838 -0.2079017  0.1428929 22.5916755 
## 
## Number of Observations: 25612
## Number of Groups: 
##              g Species %in% g 
##              1             30
#BodyMass Standardised
lm_bodymass1 <- lm(KUD50 ~ BodyMassStd, data=week_kuds)
glm_bodymass1 <- glm(KUD50 ~ BodyMassStd, data=week_kuds, family=Gamma(link="log"))
gam_bodymass1 <- gam(KUD50 ~ BodyMassStd, data=week_kuds, family=Gamma(link="log"))
glmmF_bodymass1 <- glmmTMB(KUD50 ~ BodyMassStd + (1|File), data=week_kuds, family=Gamma(link="log"))
glmmT_bodymass1 <- glmmTMB(KUD50 ~ BodyMassStd + (1|Transmitter), data=week_kuds, family=Gamma(link="log"))
glmmS_bodymass1 <- glmmTMB(KUD50 ~ BodyMassStd + (1|Species), data=week_kuds, family=Gamma(link="log"))
gammF_bodymass1 <- gamm(KUD50 ~ s(BodyMassStd), random=list(File=~1), data= week_kuds, family=Gamma(link="log"))   #Preference for gmcv package since gamm4 uses glm methods
## 
##  Maximum number of PQL iterations:  20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
#gamm4F_bodymass1 <- gamm4(KUD50 ~ s(BodyMassStd), random = ~(1|File), data = week_kuds, family=Gamma(link="log"))
gammT_bodymass1 <- gamm(KUD50 ~ s(BodyMassStd), random=list(Transmitter=~1), data= week_kuds, family=Gamma(link="log"))   #Preference for gmcv package since gamm4 uses glm methods
## 
##  Maximum number of PQL iterations:  20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
#gamm4T_bodymass1 <- gamm4(KUD50 ~ s(BodyMassStd), random = ~(1|Transmitter), data = week_kuds, family=Gamma(link="log"))
gammS_bodymass1 <- gamm(KUD50 ~ s(BodyMassStd), random=list(Species=~1), data= week_kuds, family=Gamma(link="log"))   #Preference for gmcv package since gamm4 uses glm methods
## 
##  Maximum number of PQL iterations:  20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
#gamm4S_bodymass1 <- gamm4(KUD50 ~ s(BodyMassStd), random = ~(1|Species), data = week_kuds, family=Gamma(link="log"))

AIC(lm_bodymass1, glm_bodymass1, gam_bodymass1, glmmF_bodymass1, glmmT_bodymass1, glmmS_bodymass1)
##                 df       AIC
## lm_bodymass1     3 -31877.38
## glm_bodymass1    3 -52704.33
## gam_bodymass1    3 -47446.11
## glmmF_bodymass1  4 -65875.79
## glmmT_bodymass1  4 -75920.93
## glmmS_bodymass1  4 -61148.93
summary(gammF_bodymass1$lme)
## Linear mixed-effects model fit by maximum likelihood
##   Data: data 
##        AIC     BIC    logLik
##   22765.65 22806.4 -11377.82
## 
## Random effects:
##  Formula: ~Xr - 1 | g
##  Structure: pdIdnot
##              Xr1      Xr2      Xr3      Xr4      Xr5      Xr6      Xr7      Xr8
## StdDev: 1.675688 1.675688 1.675688 1.675688 1.675688 1.675688 1.675688 1.675688
## 
##  Formula: ~1 | File %in% g
##         (Intercept)  Residual
## StdDev:   0.3230223 0.3752228
## 
## Variance function:
##  Structure: fixed weights
##  Formula: ~invwt 
## Fixed effects:  list(fixed) 
##                         Value  Std.Error    DF    t-value p-value
## X(Intercept)       -1.4907706 0.04734959 25563 -31.484343       0
## Xs(BodyMassStd)Fx1 -0.3614522 0.08382403 25563  -4.312036       0
##  Correlation: 
##                    X(Int)
## Xs(BodyMassStd)Fx1 0.005 
## 
## Standardized Within-Group Residuals:
##        Min         Q1        Med         Q3        Max 
## -1.8017544 -0.4697812 -0.2306646  0.1349153 24.2120975 
## 
## Number of Observations: 25612
## Number of Groups: 
##           g File %in% g 
##           1          48
summary(gammT_bodymass1$lme)
## Linear mixed-effects model fit by maximum likelihood
##   Data: data 
##        AIC     BIC    logLik
##   10267.54 10308.3 -5128.772
## 
## Random effects:
##  Formula: ~Xr - 1 | g
##  Structure: pdIdnot
##                 Xr1         Xr2         Xr3         Xr4         Xr5         Xr6
## StdDev: 0.000134808 0.000134808 0.000134808 0.000134808 0.000134808 0.000134808
##                 Xr7         Xr8
## StdDev: 0.000134808 0.000134808
## 
##  Formula: ~1 | Transmitter %in% g
##         (Intercept)  Residual
## StdDev:   0.3322417 0.2805759
## 
## Variance function:
##  Structure: fixed weights
##  Formula: ~invwt 
## Fixed effects:  list(fixed) 
##                         Value   Std.Error    DF    t-value p-value
## X(Intercept)       -1.4863807 0.011918936 24761 -124.70750   0.000
## Xs(BodyMassStd)Fx1  0.0102556 0.008470971 24761    1.21067   0.226
##  Correlation: 
##                    X(Int)
## Xs(BodyMassStd)Fx1 -0.064
## 
## Standardized Within-Group Residuals:
##         Min          Q1         Med          Q3         Max 
## -2.56391491 -0.39721582 -0.11012062  0.08031375 16.89270454 
## 
## Number of Observations: 25612
## Number of Groups: 
##                  g Transmitter %in% g 
##                  1                850
summary(gammS_bodymass1$lme)
## Linear mixed-effects model fit by maximum likelihood
##   Data: data 
##        AIC     BIC    logLik
##   30072.04 30112.8 -15031.02
## 
## Random effects:
##  Formula: ~Xr - 1 | g
##  Structure: pdIdnot
##              Xr1      Xr2      Xr3      Xr4      Xr5      Xr6      Xr7      Xr8
## StdDev: 1.803092 1.803092 1.803092 1.803092 1.803092 1.803092 1.803092 1.803092
## 
##  Formula: ~1 | Species %in% g
##         (Intercept)  Residual
## StdDev:   0.3365053 0.4335209
## 
## Variance function:
##  Structure: fixed weights
##  Formula: ~invwt 
## Fixed effects:  list(fixed) 
##                         Value  Std.Error    DF    t-value p-value
## X(Intercept)       -1.4561697 0.06262829 25581 -23.250989       0
## Xs(BodyMassStd)Fx1 -0.4317329 0.09563974 25581  -4.514158       0
##  Correlation: 
##                    X(Int)
## Xs(BodyMassStd)Fx1 0.007 
## 
## Standardized Within-Group Residuals:
##        Min         Q1        Med         Q3        Max 
## -1.5658935 -0.4997306 -0.2348428  0.1312459 21.6077413 
## 
## Number of Observations: 25612
## Number of Groups: 
##              g Species %in% g 
##              1             30
#Longevity
lm_longevity1 <- lm(KUD50 ~ Longevity, data=week_kuds)
glm_longevity1 <- glm(KUD50 ~ Longevity, data=week_kuds, family=Gamma(link="log"))
gam_longevity1 <- gam(KUD50 ~ Longevity, data=week_kuds, family=Gamma(link="log"))
glmmF_longevity1 <- glmmTMB(KUD50 ~ Longevity + (1|File), data=week_kuds, family=Gamma(link="log"))
glmmT_longevity1 <- glmmTMB(KUD50 ~ Longevity + (1|Transmitter), data=week_kuds, family=Gamma(link="log"))
glmmS_longevity1 <- glmmTMB(KUD50 ~ Longevity + (1|Species), data=week_kuds, family=Gamma(link="log"))
gammF_longevity1 <- gamm(KUD50 ~ s(Longevity), random=list(File=~1), data= week_kuds, family=Gamma(link="log"))   #Preference for gmcv package since gamm4 uses glm methods
## 
##  Maximum number of PQL iterations:  20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
#gamm4F_longevity1 <- gamm4(KUD50 ~ s(Longevity), random = ~(1|File), data = week_kuds, family=Gamma(link="log"))
gammT_longevity1 <- gamm(KUD50 ~ s(Longevity), random=list(Transmitter=~1), data= week_kuds, family=Gamma(link="log"))   #Preference for gmcv package since gamm4 uses glm methods
## 
##  Maximum number of PQL iterations:  20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
#gamm4T_longevity1 <- gamm4(KUD50 ~ s(Longevity), random = ~(1|Transmitter), data = week_kuds, family=Gamma(link="log"))
gammS_longevity1 <- gamm(KUD50 ~ s(Longevity), random=list(Species=~1), data= week_kuds, family=Gamma(link="log"))   #Preference for gmcv package since gamm4 uses glm methods
## 
##  Maximum number of PQL iterations:  20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
#gamm4S_longevity1 <- gamm4(KUD50 ~ s(Longevity), random = ~(1|Species), data = week_kuds, family=Gamma(link="log"))

AIC(lm_longevity1, glm_longevity1, gam_longevity1, glmmF_longevity1, glmmT_longevity1, glmmS_longevity1)
##                  df       AIC
## lm_longevity1     3 -32045.20
## glm_longevity1    3 -53090.12
## gam_longevity1    3 -47897.16
## glmmF_longevity1  4 -65829.55
## glmmT_longevity1  4 -75930.00
## glmmS_longevity1  4 -61149.69
summary(gammF_longevity1$lme)
## Linear mixed-effects model fit by maximum likelihood
##   Data: data 
##        AIC     BIC    logLik
##   23189.64 23230.4 -11589.82
## 
## Random effects:
##  Formula: ~Xr - 1 | g
##  Structure: pdIdnot
##                 Xr1         Xr2         Xr3         Xr4         Xr5         Xr6
## StdDev: 0.004302762 0.004302762 0.004302762 0.004302762 0.004302762 0.004302762
##                 Xr7         Xr8
## StdDev: 0.004302762 0.004302762
## 
##  Formula: ~1 | File %in% g
##         (Intercept)  Residual
## StdDev:   0.3111037 0.3786781
## 
## Variance function:
##  Structure: fixed weights
##  Formula: ~invwt 
## Fixed effects:  list(fixed) 
##                       Value  Std.Error    DF   t-value p-value
## X(Intercept)     -1.4941362 0.04667071 25564 -32.01443  0.0000
## Xs(Longevity)Fx1  0.0644094 0.05228794    46   1.23182  0.2243
##  Correlation: 
##                  X(Int)
## Xs(Longevity)Fx1 -0.21 
## 
## Standardized Within-Group Residuals:
##        Min         Q1        Med         Q3        Max 
## -1.7704567 -0.4788453 -0.2238381  0.1224854 24.8902216 
## 
## Number of Observations: 25612
## Number of Groups: 
##           g File %in% g 
##           1          48
summary(gammT_longevity1$lme)
## Linear mixed-effects model fit by maximum likelihood
##   Data: data 
##        AIC      BIC    logLik
##   10223.12 10263.88 -5106.562
## 
## Random effects:
##  Formula: ~Xr - 1 | g
##  Structure: pdIdnot
##            Xr1    Xr2    Xr3    Xr4    Xr5    Xr6    Xr7    Xr8
## StdDev: 1.1358 1.1358 1.1358 1.1358 1.1358 1.1358 1.1358 1.1358
## 
##  Formula: ~1 | Transmitter %in% g
##         (Intercept)  Residual
## StdDev:   0.3195612 0.2805209
## 
## Variance function:
##  Structure: fixed weights
##  Formula: ~invwt 
## Fixed effects:  list(fixed) 
##                       Value  Std.Error    DF    t-value p-value
## X(Intercept)     -1.4925891 0.01268497 24761 -117.66595  0.0000
## Xs(Longevity)Fx1 -0.2048714 0.18981738 24761   -1.07931  0.2805
##  Correlation: 
##                  X(Int)
## Xs(Longevity)Fx1 -0.003
## 
## Standardized Within-Group Residuals:
##         Min          Q1         Med          Q3         Max 
## -2.56380928 -0.40303735 -0.11176179  0.08279978 16.91852508 
## 
## Number of Observations: 25612
## Number of Groups: 
##                  g Transmitter %in% g 
##                  1                850
summary(gammS_longevity1$lme)
## Linear mixed-effects model fit by maximum likelihood
##   Data: data 
##        AIC      BIC    logLik
##   30353.56 30394.31 -15171.78
## 
## Random effects:
##  Formula: ~Xr - 1 | g
##  Structure: pdIdnot
##                  Xr1          Xr2          Xr3          Xr4          Xr5
## StdDev: 0.0006308293 0.0006308293 0.0006308293 0.0006308293 0.0006308293
##                  Xr6          Xr7          Xr8
## StdDev: 0.0006308293 0.0006308293 0.0006308293
## 
##  Formula: ~1 | Species %in% g
##         (Intercept)  Residual
## StdDev:   0.3268438 0.4362716
## 
## Variance function:
##  Structure: fixed weights
##  Formula: ~invwt 
## Fixed effects:  list(fixed) 
##                       Value  Std.Error    DF    t-value p-value
## X(Intercept)     -1.4700166 0.06584704 25582 -22.324718  0.0000
## Xs(Longevity)Fx1  0.0749604 0.08582418    28   0.873419  0.3899
##  Correlation: 
##                  X(Int)
## Xs(Longevity)Fx1 -0.383
## 
## Standardized Within-Group Residuals:
##        Min         Q1        Med         Q3        Max 
## -1.5360005 -0.5037871 -0.2220684  0.1272389 22.7508061 
## 
## Number of Observations: 25612
## Number of Groups: 
##              g Species %in% g 
##              1             30
#Vulnerability
lm_vulnerability1 <- lm(KUD50 ~ Vulnerability, data=week_kuds)
glm_vulnerability1 <- glm(KUD50 ~ Vulnerability, data=week_kuds, family=Gamma(link="log"))
gam_vulnerability1 <- gam(KUD50 ~ Vulnerability, data=week_kuds, family=Gamma(link="log"))
glmmF_vulnerability1 <- glmmTMB(KUD50 ~ Vulnerability + (1|File), data=week_kuds, family=Gamma(link="log"))
glmmT_vulnerability1 <- glmmTMB(KUD50 ~ Vulnerability + (1|Transmitter), data=week_kuds, family=Gamma(link="log"))
glmmS_vulnerability1 <- glmmTMB(KUD50 ~ Vulnerability + (1|Species), data=week_kuds, family=Gamma(link="log"))
gammF_vulnerability1 <- gamm(KUD50 ~ s(Vulnerability), random=list(File=~1), data= week_kuds, family=Gamma(link="log"))   #Preference for gmcv package since gamm4 uses glm methods
## 
##  Maximum number of PQL iterations:  20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
#gamm4F_vulnerability1 <- gamm4(KUD50 ~ s(Vulnerability), random = ~(1|File), data = week_kuds, family=Gamma(link="log"))
gammT_vulnerability1 <- gamm(KUD50 ~ s(Vulnerability), random=list(Transmitter=~1), data= week_kuds, family=Gamma(link="log"))   #Preference for gmcv package since gamm4 uses glm methods
## 
##  Maximum number of PQL iterations:  20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
#gamm4T_vulnerability1 <- gamm4(KUD50 ~ s(Vulnerability), random = ~(1|Transmitter), data = week_kuds, family=Gamma(link="log"))
gammS_vulnerability1 <- gamm(KUD50 ~ s(Vulnerability), random=list(Species=~1), data= week_kuds, family=Gamma(link="log"))   #Preference for gmcv package since gamm4 uses glm methods
## 
##  Maximum number of PQL iterations:  20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
#gamm4S_vulnerability1 <- gamm4(KUD50 ~ s(Vulnerability), random = ~(1|Species), data = week_kuds, family=Gamma(link="log"))

AIC(lm_vulnerability1, glm_vulnerability1, gam_vulnerability1, glmmF_vulnerability1, glmmT_vulnerability1, glmmS_vulnerability1)
##                      df       AIC
## lm_vulnerability1     3 -32005.03
## glm_vulnerability1    3 -52955.22
## gam_vulnerability1    3 -47660.03
## glmmF_vulnerability1  4 -65829.82
## glmmT_vulnerability1  4 -75921.04
## glmmS_vulnerability1  4 -61151.52
summary(gammF_vulnerability1$lme)
## Linear mixed-effects model fit by maximum likelihood
##   Data: data 
##        AIC      BIC    logLik
##   23189.07 23229.83 -11589.54
## 
## Random effects:
##  Formula: ~Xr - 1 | g
##  Structure: pdIdnot
##                 Xr1         Xr2         Xr3         Xr4         Xr5         Xr6
## StdDev: 0.001932727 0.001932727 0.001932727 0.001932727 0.001932727 0.001932727
##                 Xr7         Xr8
## StdDev: 0.001932727 0.001932727
## 
##  Formula: ~1 | File %in% g
##         (Intercept)  Residual
## StdDev:   0.3109426 0.3786742
## 
## Variance function:
##  Structure: fixed weights
##  Formula: ~invwt 
## Fixed effects:  list(fixed) 
##                           Value  Std.Error    DF   t-value p-value
## X(Intercept)         -1.4568054 0.04938715 25564 -29.49766  0.0000
## Xs(Vulnerability)Fx1  0.0495008 0.03711036    46   1.33388  0.1888
##  Correlation: 
##                      X(Int)
## Xs(Vulnerability)Fx1 0.383 
## 
## Standardized Within-Group Residuals:
##        Min         Q1        Med         Q3        Max 
## -1.7700272 -0.4789700 -0.2240155  0.1229565 24.8951430 
## 
## Number of Observations: 25612
## Number of Groups: 
##           g File %in% g 
##           1          48
summary(gammT_vulnerability1$lme)
## Linear mixed-effects model fit by maximum likelihood
##   Data: data 
##        AIC      BIC    logLik
##   10266.08 10306.83 -5128.039
## 
## Random effects:
##  Formula: ~Xr - 1 | g
##  Structure: pdIdnot
##                 Xr1         Xr2         Xr3         Xr4         Xr5         Xr6
## StdDev: 7.45902e-05 7.45902e-05 7.45902e-05 7.45902e-05 7.45902e-05 7.45902e-05
##                 Xr7         Xr8
## StdDev: 7.45902e-05 7.45902e-05
## 
##  Formula: ~1 | Transmitter %in% g
##         (Intercept)  Residual
## StdDev:   0.3314166 0.2805896
## 
## Variance function:
##  Structure: fixed weights
##  Formula: ~invwt 
## Fixed effects:  list(fixed) 
##                           Value  Std.Error    DF    t-value p-value
## X(Intercept)         -1.4887249 0.01205047 24761 -123.54085  0.0000
## Xs(Vulnerability)Fx1 -0.0165179 0.01067642 24761   -1.54713  0.1218
##  Correlation: 
##                      X(Int)
## Xs(Vulnerability)Fx1 0.174 
## 
## Standardized Within-Group Residuals:
##         Min          Q1         Med          Q3         Max 
## -2.56458541 -0.39765169 -0.11013309  0.08013874 16.89163537 
## 
## Number of Observations: 25612
## Number of Groups: 
##                  g Transmitter %in% g 
##                  1                850
summary(gammS_vulnerability1$lme)
## Linear mixed-effects model fit by maximum likelihood
##   Data: data 
##       AIC      BIC    logLik
##   30351.3 30392.06 -15170.65
## 
## Random effects:
##  Formula: ~Xr - 1 | g
##  Structure: pdIdnot
##                 Xr1         Xr2         Xr3         Xr4         Xr5         Xr6
## StdDev: 0.006845411 0.006845411 0.006845411 0.006845411 0.006845411 0.006845411
##                 Xr7         Xr8
## StdDev: 0.006845411 0.006845411
## 
##  Formula: ~1 | Species %in% g
##         (Intercept)  Residual
## StdDev:   0.3185965 0.4362648
## 
## Variance function:
##  Structure: fixed weights
##  Formula: ~invwt 
## Fixed effects:  list(fixed) 
##                           Value  Std.Error    DF   t-value p-value
## X(Intercept)         -1.3922752 0.06867512 25582 -20.27336   0.000
## Xs(Vulnerability)Fx1  0.0700168 0.04340812    28   1.61299   0.118
##  Correlation: 
##                      X(Int)
## Xs(Vulnerability)Fx1 0.501 
## 
## Standardized Within-Group Residuals:
##        Min         Q1        Med         Q3        Max 
## -1.5353903 -0.5038038 -0.2214454  0.1284833 22.7568854 
## 
## Number of Observations: 25612
## Number of Groups: 
##              g Species %in% g 
##              1             30
#Troph
lm_troph1 <- lm(KUD50 ~ Troph, data=week_kuds)
glm_troph1 <- glm(KUD50 ~ Troph, data=week_kuds, family=Gamma(link="log"))
gam_troph1 <- gam(KUD50 ~ Troph, data=week_kuds, family=Gamma(link="log"))
glmmF_troph1 <- glmmTMB(KUD50 ~ Troph + (1|File), data=week_kuds, family=Gamma(link="log"))
glmmT_troph1 <- glmmTMB(KUD50 ~ Troph + (1|Transmitter), data=week_kuds, family=Gamma(link="log"))
glmmS_troph1 <- glmmTMB(KUD50 ~ Troph + (1|Species), data=week_kuds, family=Gamma(link="log"))
gammF_troph1 <- gamm(KUD50 ~ s(Troph), random=list(File=~1), data= week_kuds, family=Gamma(link="log"))   ##Preference for gmcv package since gamm4 uses glm methods
## 
##  Maximum number of PQL iterations:  20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
#gamm4F_troph1 <- gamm4(KUD50 ~ s(Troph), random = ~(1|File), data = week_kuds, family=Gamma(link="log"))
gammT_troph1 <- gamm(KUD50 ~ s(Troph), random=list(Transmitter=~1), data= week_kuds, family=Gamma(link="log"))   #Preference for gmcv package since gamm4 uses glm methods
## 
##  Maximum number of PQL iterations:  20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
#gamm4T_troph1 <- gamm4(KUD50 ~ s(Troph), random = ~(1|Transmitter), data = week_kuds, family=Gamma(link="log"))
gammS_troph1 <- gamm(KUD50 ~ s(Troph), random=list(Species=~1), data= week_kuds, family=Gamma(link="log"))   #Preference for gmcv package since gamm4 uses glm methods
## 
##  Maximum number of PQL iterations:  20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
#gamm4S_troph1 <- gamm4(KUD50 ~ s(Troph), random = ~(1|Species), data = week_kuds, family=Gamma(link="log"))

AIC(lm_troph1, glm_troph1, gam_troph1, glmmF_troph1, glmmT_troph1, glmmS_troph1)
##              df       AIC
## lm_troph1     3 -31856.46
## glm_troph1    3 -52657.77
## gam_troph1    3 -47370.47
## glmmF_troph1  4 -65832.43
## glmmT_troph1  4 -75923.01
## glmmS_troph1  4 -61153.70
summary(gammF_troph1$lme)
## Linear mixed-effects model fit by maximum likelihood
##   Data: data 
##        AIC      BIC    logLik
##   23187.82 23228.57 -11588.91
## 
## Random effects:
##  Formula: ~Xr - 1 | g
##  Structure: pdIdnot
##                 Xr1         Xr2         Xr3         Xr4         Xr5         Xr6
## StdDev: 0.007135181 0.007135181 0.007135181 0.007135181 0.007135181 0.007135181
##                 Xr7         Xr8
## StdDev: 0.007135181 0.007135181
## 
##  Formula: ~1 | File %in% g
##         (Intercept)  Residual
## StdDev:   0.3021429 0.3786846
## 
## Variance function:
##  Structure: fixed weights
##  Formula: ~invwt 
## Fixed effects:  list(fixed) 
##                   Value  Std.Error    DF   t-value p-value
## X(Intercept) -1.4575608 0.04580577 25564 -31.82046  0.0000
## Xs(Troph)Fx1  0.0942921 0.04422081    46   2.13230  0.0384
##  Correlation: 
##              X(Int)
## Xs(Troph)Fx1 0.249 
## 
## Standardized Within-Group Residuals:
##        Min         Q1        Med         Q3        Max 
## -1.7709195 -0.4790667 -0.2240647  0.1225451 24.8930121 
## 
## Number of Observations: 25612
## Number of Groups: 
##           g File %in% g 
##           1          48
summary(gammT_troph1$lme)
## Linear mixed-effects model fit by maximum likelihood
##   Data: data 
##        AIC      BIC    logLik
##   10132.52 10173.28 -5061.262
## 
## Random effects:
##  Formula: ~Xr - 1 | g
##  Structure: pdIdnot
##              Xr1      Xr2      Xr3      Xr4      Xr5      Xr6      Xr7      Xr8
## StdDev: 1.453333 1.453333 1.453333 1.453333 1.453333 1.453333 1.453333 1.453333
## 
##  Formula: ~1 | Transmitter %in% g
##         (Intercept)  Residual
## StdDev:   0.3038602 0.2804331
## 
## Variance function:
##  Structure: fixed weights
##  Formula: ~invwt 
## Fixed effects:  list(fixed) 
##                   Value  Std.Error    DF    t-value p-value
## X(Intercept) -1.4836458 0.01234898 24761 -120.14318   0e+00
## Xs(Troph)Fx1  0.9243041 0.26110074 24761    3.54003   4e-04
##  Correlation: 
##              X(Int)
## Xs(Troph)Fx1 -0.173
## 
## Standardized Within-Group Residuals:
##         Min          Q1         Med          Q3         Max 
## -2.56446832 -0.40144680 -0.11106743  0.08418724 16.81202429 
## 
## Number of Observations: 25612
## Number of Groups: 
##                  g Transmitter %in% g 
##                  1                850
summary(gammS_troph1$lme)
## Linear mixed-effects model fit by maximum likelihood
##   Data: data 
##        AIC      BIC    logLik
##   30349.77 30390.52 -15169.88
## 
## Random effects:
##  Formula: ~Xr - 1 | g
##  Structure: pdIdnot
##                 Xr1         Xr2         Xr3         Xr4         Xr5         Xr6
## StdDev: 0.008900372 0.008900372 0.008900372 0.008900372 0.008900372 0.008900372
##                 Xr7         Xr8
## StdDev: 0.008900372 0.008900372
## 
##  Formula: ~1 | Species %in% g
##         (Intercept)  Residual
## StdDev:   0.3060275 0.4362715
## 
## Variance function:
##  Structure: fixed weights
##  Formula: ~invwt 
## Fixed effects:  list(fixed) 
##                   Value  Std.Error    DF    t-value p-value
## X(Intercept) -1.4167364 0.05871496 25582 -24.129054  0.0000
## Xs(Troph)Fx1  0.1183704 0.05262428    28   2.249349  0.0325
##  Correlation: 
##              X(Int)
## Xs(Troph)Fx1 0.234 
## 
## Standardized Within-Group Residuals:
##        Min         Q1        Med         Q3        Max 
## -1.5365063 -0.5036437 -0.2214987  0.1283573 22.7547814 
## 
## Number of Observations: 25612
## Number of Groups: 
##              g Species %in% g 
##              1             30
#Habitat
lm_habitat1 <- lm(KUD50 ~ Habitat, data=week_kuds)
glm_habitat1 <- glm(KUD50 ~ Habitat, data=week_kuds, family=Gamma(link="log"))
gam_habitat1 <- gam(KUD50 ~ Habitat, data=week_kuds, family=Gamma(link="log"))
glmmF_habitat1 <- glmmTMB(KUD50 ~ Habitat + (1|File), data=week_kuds, family=Gamma(link="log"))
glmmT_habitat1 <- glmmTMB(KUD50 ~ Habitat + (1|Transmitter), data=week_kuds, family=Gamma(link="log"))
glmmS_habitat1 <- glmmTMB(KUD50 ~ Habitat + (1|Species), data=week_kuds, family=Gamma(link="log"))
gammF_habitat1 <- gamm(KUD50 ~ Habitat, random=list(File=~1), data= week_kuds, family=Gamma(link="log"))   #Preference for gmcv package since gamm4 uses glm methods
## 
##  Maximum number of PQL iterations:  20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
#gamm4F_habitat1 <- gamm4(KUD50 ~ Habitat, random = ~(1|File), data = week_kuds, family=Gamma(link="log"))
gammT_habitat1 <- gamm(KUD50 ~ Habitat, random=list(Transmitter=~1), data= week_kuds, family=Gamma(link="log"))   #Preference for gmcv package since gamm4 uses glm methods
## 
##  Maximum number of PQL iterations:  20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
#gamm4T_habitat1 <- gamm4(KUD50 ~ Habitat, random = ~(1|Transmitter), data = week_kuds, family=Gamma(link="log"))
gammS_habitat1 <- gamm(KUD50 ~ Habitat, random=list(Species=~1), data= week_kuds, family=Gamma(link="log"))   #Preference for gmcv package since gamm4 uses glm methods
## 
##  Maximum number of PQL iterations:  20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
#gamm4S_habitat1 <- gamm4(KUD50 ~ Habitat, random = ~(1|Species), data = week_kuds, family=Gamma(link="log"))

AIC(lm_habitat1, glm_habitat1, gam_habitat1, glmmF_habitat1, glmmT_habitat1, glmmS_habitat1)
##                df       AIC
## lm_habitat1     4 -32396.89
## glm_habitat1    4 -53780.41
## gam_habitat1    4 -48992.27
## glmmF_habitat1  5 -65850.73
## glmmT_habitat1  5 -75984.63
## glmmS_habitat1  5 -61164.81
summary(gammF_habitat1$lme)
## Linear mixed-effects model fit by maximum likelihood
##   Data: data 
##        AIC      BIC    logLik
##   23167.91 23208.66 -11578.95
## 
## Random effects:
##  Formula: ~1 | File
##         (Intercept)  Residual
## StdDev:   0.2443296 0.3786823
## 
## Variance function:
##  Structure: fixed weights
##  Formula: ~invwt 
## Fixed effects:  list(fixed) 
##                              Value  Std.Error    DF    t-value p-value
## X(Intercept)            -1.4259941 0.06401175 25564 -22.277067  0.0000
## XHabitatdemersal        -0.1985912 0.08033184    45  -2.472135  0.0173
## XHabitatpelagic-neritic  0.4191038 0.11928896    45   3.513350  0.0010
##  Correlation: 
##                         X(Int) XHbttd
## XHabitatdemersal        -0.797       
## XHabitatpelagic-neritic -0.537  0.428
## 
## Standardized Within-Group Residuals:
##        Min         Q1        Med         Q3        Max 
## -1.7722325 -0.4793527 -0.2234567  0.1233451 24.9069722 
## 
## Number of Observations: 25612
## Number of Groups: 48
summary(gammT_habitat1$lme)
## Linear mixed-effects model fit by maximum likelihood
##   Data: data 
##        AIC      BIC    logLik
##   10179.97 10220.73 -5084.987
## 
## Random effects:
##  Formula: ~1 | Transmitter
##         (Intercept) Residual
## StdDev:   0.3192479 0.280432
## 
## Variance function:
##  Structure: fixed weights
##  Formula: ~invwt 
## Fixed effects:  list(fixed) 
##                              Value  Std.Error    DF   t-value p-value
## X(Intercept)            -1.4521426 0.01666122 24761 -87.15706       0
## XHabitatdemersal        -0.1138321 0.02350036 24761  -4.84384       0
## XHabitatpelagic-neritic  0.2326868 0.04417678   848   5.26718       0
##  Correlation: 
##                         X(Int) XHbttd
## XHabitatdemersal        -0.697       
## XHabitatpelagic-neritic -0.377  0.263
## 
## Standardized Within-Group Residuals:
##         Min          Q1         Med          Q3         Max 
## -2.55905205 -0.39695996 -0.10939032  0.08198035 17.05296469 
## 
## Number of Observations: 25612
## Number of Groups: 850
summary(gammS_habitat1$lme)
## Linear mixed-effects model fit by maximum likelihood
##   Data: data 
##       AIC      BIC   logLik
##   30333.2 30373.95 -15161.6
## 
## Random effects:
##  Formula: ~1 | Species
##         (Intercept)  Residual
## StdDev:   0.2456872 0.4362372
## 
## Variance function:
##  Structure: fixed weights
##  Formula: ~invwt 
## Fixed effects:  list(fixed) 
##                              Value  Std.Error    DF    t-value p-value
## X(Intercept)            -1.4555964 0.08308074 25582 -17.520263  0.0000
## XHabitatdemersal        -0.1437323 0.10493251    27  -1.369759  0.1820
## XHabitatpelagic-neritic  0.4808568 0.13893988    27   3.460899  0.0018
##  Correlation: 
##                         X(Int) XHbttd
## XHabitatdemersal        -0.792       
## XHabitatpelagic-neritic -0.598  0.473
## 
## Standardized Within-Group Residuals:
##        Min         Q1        Med         Q3        Max 
## -1.5378088 -0.5036260 -0.2217697  0.1280249 22.7700811 
## 
## Number of Observations: 25612
## Number of Groups: 30
#Migration
lm_migration1 <- lm(KUD50 ~ Migration, data=week_kuds)
glm_migration1 <- glm(KUD50 ~ Migration, data=week_kuds, family=Gamma(link="log"))
gam_migration1 <- gam(KUD50 ~ Migration, data=week_kuds, family=Gamma(link="log"))
glmmF_migration1 <- glmmTMB(KUD50 ~ Migration + (1|File), data=week_kuds, family=Gamma(link="log"))
glmmT_migration1 <- glmmTMB(KUD50 ~ Migration + (1|Transmitter), data=week_kuds, family=Gamma(link="log"))
glmmS_migration1 <- glmmTMB(KUD50 ~ Migration + (1|Species), data=week_kuds, family=Gamma(link="log"))
gammF_migration1 <- gamm(KUD50 ~ Migration, random=list(File=~1), data= week_kuds, family=Gamma(link="log"))   #Preference for gmcv package since gamm4 uses glm methods
## 
##  Maximum number of PQL iterations:  20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
#gamm4F_migration1 <- gamm4(KUD50 ~ Migration, random = ~(1|File), data = week_kuds, family=Gamma(link="log"))
gammT_migration1 <- gamm(KUD50 ~ Migration, random=list(Transmitter=~1), data= week_kuds, family=Gamma(link="log"))   #Preference for gmcv package since gamm4 uses glm methods
## 
##  Maximum number of PQL iterations:  20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
#gamm4T_migration1 <- gamm4(KUD50 ~ Migration, random = ~(1|Transmitter), data = week_kuds, family=Gamma(link="log"))
gammS_migration1 <- gamm(KUD50 ~ Migration, random=list(Species=~1), data= week_kuds, family=Gamma(link="log"))   #Preference for gmcv package since gamm4 uses glm methods
## 
##  Maximum number of PQL iterations:  20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
#gamm4S_migration1 <- gamm4(KUD50 ~ Migration, random = ~(1|Species), data = week_kuds, family=Gamma(link="log"))

AIC(lm_migration1, glm_migration1, gam_migration1, glmmF_migration1, glmmT_migration1, glmmS_migration1)
##                  df       AIC
## lm_migration1     3 -32191.53
## glm_migration1    3 -53333.40
## gam_migration1    3 -48532.86
## glmmF_migration1  4 -65839.26
## glmmT_migration1  4 -75937.60
## glmmS_migration1  4 -61157.96
summary(gammF_migration1$lme)
## Linear mixed-effects model fit by maximum likelihood
##   Data: data 
##        AIC      BIC    logLik
##   23180.12 23212.73 -11586.06
## 
## Random effects:
##  Formula: ~1 | File
##         (Intercept)  Residual
## StdDev:   0.2811582 0.3786919
## 
## Variance function:
##  Structure: fixed weights
##  Formula: ~invwt 
## Fixed effects:  list(fixed) 
##                              Value  Std.Error    DF   t-value p-value
## X(Intercept)            -1.5773729 0.04935664 25564 -31.95868   0e+00
## XMigrationoceanodromous  0.3200494 0.09041543    46   3.53977   9e-04
##  Correlation: 
##                         X(Int)
## XMigrationoceanodromous -0.546
## 
## Standardized Within-Group Residuals:
##        Min         Q1        Med         Q3        Max 
## -1.7707688 -0.4797200 -0.2235529  0.1234385 24.8976334 
## 
## Number of Observations: 25612
## Number of Groups: 48
summary(gammT_migration1$lme)
## Linear mixed-effects model fit by maximum likelihood
##   Data: data 
##        AIC      BIC    logLik
##   10241.27 10273.87 -5116.634
## 
## Random effects:
##  Formula: ~1 | Transmitter
##         (Intercept)  Residual
## StdDev:   0.3279284 0.2805539
## 
## Variance function:
##  Structure: fixed weights
##  Formula: ~invwt 
## Fixed effects:  list(fixed) 
##                              Value  Std.Error    DF   t-value p-value
## X(Intercept)            -1.5312416 0.01572497 24762 -97.37647       0
## XMigrationoceanodromous  0.1035623 0.02366378   848   4.37640       0
##  Correlation: 
##                         X(Int)
## XMigrationoceanodromous -0.665
## 
## Standardized Within-Group Residuals:
##         Min          Q1         Med          Q3         Max 
## -2.56127361 -0.39901226 -0.10991915  0.08104294 16.92022478 
## 
## Number of Observations: 25612
## Number of Groups: 850
summary(gammS_migration1$lme)
## Linear mixed-effects model fit by maximum likelihood
##   Data: data 
##        AIC      BIC    logLik
##   30339.13 30371.73 -15165.56
## 
## Random effects:
##  Formula: ~1 | Species
##         (Intercept)  Residual
## StdDev:   0.2844533 0.4362337
## 
## Variance function:
##  Structure: fixed weights
##  Formula: ~invwt 
## Fixed effects:  list(fixed) 
##                             Value  Std.Error    DF    t-value p-value
## X(Intercept)            -1.562664 0.06401759 25582 -24.409920  0.0000
## XMigrationoceanodromous  0.370545 0.11494195    28   3.223757  0.0032
##  Correlation: 
##                         X(Int)
## XMigrationoceanodromous -0.557
## 
## Standardized Within-Group Residuals:
##        Min         Q1        Med         Q3        Max 
## -1.5365139 -0.5036895 -0.2220609  0.1279743 22.7623218 
## 
## Number of Observations: 25612
## Number of Groups: 30
#ComImport
lm_comimport1 <- lm(KUD50 ~ ComImport, data=week_kuds)
glm_comimport1 <- glm(KUD50 ~ ComImport, data=week_kuds, family=Gamma(link="log"))
gam_comimport1 <- gam(KUD50 ~ ComImport, data=week_kuds, family=Gamma(link="log"))
glmmF_comimport1 <- glmmTMB(KUD50 ~ ComImport + (1|File), data=week_kuds, family=Gamma(link="log"))
glmmT_comimport1 <- glmmTMB(KUD50 ~ ComImport + (1|Transmitter), data=week_kuds, family=Gamma(link="log"))
glmmS_comimport1 <- glmmTMB(KUD50 ~ ComImport + (1|Species), data=week_kuds, family=Gamma(link="log"))
gammF_comimport1 <- gamm(KUD50 ~ ComImport, random=list(File=~1), data= week_kuds, family=Gamma(link="log"))   #Preference for gmcv package since gamm4 uses glm methods
## 
##  Maximum number of PQL iterations:  20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
#gamm4F_comimport1 <- gamm4(KUD50 ~ ComImport, random = ~(1|File), data = week_kuds, family=Gamma(link="log"))
gammT_comimport1 <- gamm(KUD50 ~ ComImport, random=list(Transmitter=~1), data= week_kuds, family=Gamma(link="log"))   #Preference for gmcv package since gamm4 uses glm methods
## 
##  Maximum number of PQL iterations:  20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
#gamm4T_comimport1 <- gamm4(KUD50 ~ ComImport, random = ~(1|Transmitter), data = week_kuds, family=Gamma(link="log"))
gammS_comimport1 <- gamm(KUD50 ~ ComImport, random=list(Species=~1), data= week_kuds, family=Gamma(link="log"))   #Preference for gmcv package since gamm4 uses glm methods
## 
##  Maximum number of PQL iterations:  20
## iteration 1
## iteration 2
## iteration 3
## iteration 4
#gamm4S_comimport1 <- gamm4(KUD50 ~ ComImport, random = ~(1|Species), data = week_kuds, family=Gamma(link="log"))

AIC(lm_comimport1, glm_comimport1, gam_comimport1, glmmF_comimport1, glmmT_comimport1, glmmS_comimport1)
##                  df       AIC
## lm_comimport1     4 -31846.21
## glm_comimport1    4 -52643.88
## gam_comimport1    4 -47290.79
## glmmF_comimport1  5 -65827.93
## glmmT_comimport1  5 -75942.37
## glmmS_comimport1  5 -61148.70
summary(gammF_comimport1$lme)
## Linear mixed-effects model fit by maximum likelihood
##   Data: data 
##        AIC      BIC    logLik
##   23189.53 23230.28 -11589.76
## 
## Random effects:
##  Formula: ~1 | File
##         (Intercept)  Residual
## StdDev:   0.3102903 0.3786791
## 
## Variance function:
##  Structure: fixed weights
##  Formula: ~invwt 
## Fixed effects:  list(fixed) 
##                       Value  Std.Error    DF    t-value p-value
## X(Intercept)     -1.4994421 0.06356000 25564 -23.590971  0.0000
## XComImportmedium  0.0887424 0.09945657    45   0.892273  0.3770
## XComImportminor  -0.1055033 0.13997297    45  -0.753741  0.4549
##  Correlation: 
##                  X(Int) XCmImprtmd
## XComImportmedium -0.639           
## XComImportminor  -0.454  0.290    
## 
## Standardized Within-Group Residuals:
##        Min         Q1        Med         Q3        Max 
## -1.7698920 -0.4788326 -0.2243400  0.1234447 24.8923718 
## 
## Number of Observations: 25612
## Number of Groups: 48
summary(gammT_comimport1$lme)
## Linear mixed-effects model fit by maximum likelihood
##   Data: data 
##       AIC      BIC    logLik
##   10261.3 10302.06 -5125.651
## 
## Random effects:
##  Formula: ~1 | Transmitter
##         (Intercept) Residual
## StdDev:   0.3310625 0.280572
## 
## Variance function:
##  Structure: fixed weights
##  Formula: ~invwt 
## Fixed effects:  list(fixed) 
##                       Value  Std.Error    DF    t-value p-value
## X(Intercept)     -1.4742920 0.01361534 24760 -108.28165  0.0000
## XComImportmedium -0.0174252 0.02764499 24760   -0.63032  0.5285
## XComImportminor  -0.1969604 0.04180688 24760   -4.71120  0.0000
##  Correlation: 
##                  X(Int) XCmImprtmd
## XComImportmedium -0.479           
## XComImportminor  -0.144  0.069    
## 
## Standardized Within-Group Residuals:
##         Min          Q1         Med          Q3         Max 
## -2.56384816 -0.39986609 -0.11021798  0.08085676 16.89063683 
## 
## Number of Observations: 25612
## Number of Groups: 850
summary(gammS_comimport1$lme)
## Linear mixed-effects model fit by maximum likelihood
##   Data: data 
##        AIC      BIC    logLik
##   30350.69 30391.44 -15170.34
## 
## Random effects:
##  Formula: ~1 | Species
##         (Intercept)  Residual
## StdDev:   0.3218846 0.4362546
## 
## Variance function:
##  Structure: fixed weights
##  Formula: ~invwt 
## Fixed effects:  list(fixed) 
##                       Value Std.Error    DF    t-value p-value
## X(Intercept)     -1.4083847 0.1019478 25582 -13.814759  0.0000
## XComImportmedium  0.0036375 0.1359229    27   0.026762  0.9788
## XComImportminor  -0.1964511 0.1658027    27  -1.184848  0.2464
##  Correlation: 
##                  X(Int) XCmImprtmd
## XComImportmedium -0.750           
## XComImportminor  -0.615  0.461    
## 
## Standardized Within-Group Residuals:
##        Min         Q1        Med         Q3        Max 
## -1.5357896 -0.5038047 -0.2220157  0.1279555 22.7519889 
## 
## Number of Observations: 25612
## Number of Groups: 30

Observing the AIC values of all models adjusted, we can see that the one that better fits the data is the GLMM, with the Transmitter as random effect. Looking for all the GLMM adjusted, with higher attention to the variables used as random effects, we see that the models adjusted with Transmitter are better than the ones adjusted with File, and lastly Species.

To analyse and support the importance of each variable assigned to the random effect, we also perform a random forest model, including all three variables: Transmitter, File and Species.

random_three <- rpart(KUD50 ~ Species + Transmitter + File, data = week_kuds)

summary(random_three)
## Call:
## rpart(formula = KUD50 ~ Species + Transmitter + File, data = week_kuds)
##   n= 25612 
## 
##           CP nsplit rel error    xerror       xstd
## 1 0.33008309      0 1.0000000 1.0000730 0.04804025
## 2 0.05974581      1 0.6699169 0.6961087 0.04026670
## 3 0.04250898      2 0.6101711 0.6537113 0.03813751
## 4 0.01662605      3 0.5676621 0.6172148 0.03754515
## 5 0.01000000      4 0.5510361 0.6071407 0.03647241
## 
## Variable importance
## Transmitter        File     Species 
##          69          21          10 
## 
## Node number 1: 25612 observations,    complexity param=0.3300831
##   mean=0.2267318, MSE=0.01689573 
##   left son=2 (22066 obs) right son=3 (3546 obs)
##   Primary splits:
##       Transmitter splits as  LLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLRLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLRLLLLLLLLLLLLLLLLLRLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLRLLLLLLLLLRLLLLLLRLLLLLRLLLLLLLLLLRLLLRLLLLLLLLLLLLLLLLLLLLLLLLLLLLLRLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLRLRLLLLRLLLRRLLRLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLRLLRLRLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLRLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLRRRRRRRRLRRRRLRLRLRLRRRRLLRLRLLRRRRRRRRLLRRRRRRRRLLRLRLLLRLLRRLLLRLRRLRRRRRRLRLLLRLRLLLLLRRRLLRRLLLLLLLLLLLLRLLLLLLLRRLLLRLRLLRLLLLLLLLRRRLLLLLLLLLLLLRRLLLLRRLLRRRLLLLLLRLLLLLLRLLRLLLLRRLLLRRRLRLLLLLLRRLLLLLLLLLLLLLLLLLLRRLRLLLLRRLLRLRRRLLLRLLLRRLLRRRLLRLRRLLRRLRRRLLRLLRLLRLLLLRRRRLRRLLLLLLLLLLLLLLLLLLLLRLLLLLLRLLLLLLLLLRLLLRLLRLLRLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLRLLRLLLRLLLLLLLRLLLLLLLLLLLLLLLLLLLRRL, improve=0.3300831, (0 missing)
##       File        splits as  LLRLRRLLLLLLLLLLLLLLLLRLLLLRLLLLLLRLLLLLLLRLRLLL, improve=0.2033131, (0 missing)
##       Species     splits as  RRRLRLLLRLLLLLRLRLLLLRLLLLLRLL, improve=0.1134862, (0 missing)
##   Surrogate splits:
##       File    splits as  LLLLRLLLLLLLLLLLLLLLLLRLLLLRLLLLLLRLLLLLLLLLRLLL, agree=0.912, adj=0.362, (0 split)
##       Species splits as  LLLLLLLLRLLLLLRLLLLLLRLLLLLLLL, agree=0.881, adj=0.139, (0 split)
## 
## Node number 2: 22066 observations,    complexity param=0.04250898
##   mean=0.1967949, MSE=0.003629296 
##   left son=4 (17166 obs) right son=5 (4900 obs)
##   Primary splits:
##       Transmitter splits as  LRLLRLLLLLLLLLLLLLLLLLRRRLLLRRLRRRRRLRR-LLRLLLRLLRRLLLLRRLLRRRLLRRLLLLRRLRRLRRRRRR-LLLLLRLLLRLLLLRLR-RLLLLLLLRLLRLRLRRLLLLLLRLLLRLLLLLLRRLLLLLLLLLRLRLLRLLRL-RLRLRLLLL-LRRRLR-RRRRR-LRRLLRRLRL-RRL-RLRLLLLLLLLLLLLLLLLLLRLLLLLLL-LLLLLLLLLLLRRLLLLLLLLLLLLLLLLLRRLLLLLLRLLRRLLRLL-L-LLLL-LRR--RR-LLLRLLLLLRLLLRLLLLLLLLLLLLLLLLRLRLLRLLRLRL-RL-R-LLLRLRRRLLRLLLLRLLLLLLLLLLLLLLLLLLLLLLLLRLLLLLLLLL-LLLLLLLLLLLLLLLLLLLLLLLLLLLLLL--------R----L-R-R-L----LR-L-LL--------RR--------LL-L-RLL-LL--LLR-R--L------L-RLR-L-LRRRL---LR--RRLLLLLLLLLR-RRLLLLL--LLL-R-RL-LLRRLLLR---LLLLLLLLLLLL--LLRR--LL---RLLLLR-RRLLLL-RR-RRLL--LLR---R-LLRRLL--LLLLRRLLLLLLRRRRRR--L-LLRL--LL-R---RLR-LRL--RR---LL-R--LL--R---RR-LL-LL-LLLL----L--LLRLRLLLRRRRRLRLLRLR-RRLLLR-LRRLRLLRL-RLR-RR-RR-RRRRRLLRLRLRLLLLLLLLLLLLLLLLLLLLLLLLLLLLLRRLLLLLLLLLLLLLLRLLL-LL-LLR-LRLLLLR-LRRLRRRLRLLRLLLLLLR--L, improve=0.22969690, (0 missing)
##       File        splits as  RRRRLRRLLRRLLRLLLLRLLL-RLLLRLLRLLR-LLRLLLLRLRRLL, improve=0.06237878, (0 missing)
##       Species     splits as  RRRRRLLR-LRLLLRLRRRLR-LLLLLLLL, improve=0.04785787, (0 missing)
##   Surrogate splits:
##       File    splits as  RRRLLRLLLLLLLLLLLLRLLL-RLLLRLLRLLL-LLLLLLLRLRLLL, agree=0.813, adj=0.160, (0 split)
##       Species splits as  RRLLRLLL-LRLLLRLLLLLL-LLLLLLLL, agree=0.803, adj=0.114, (0 split)
## 
## Node number 3: 3546 observations,    complexity param=0.05974581
##   mean=0.4130231, MSE=0.05916846 
##   left son=6 (2630 obs) right son=7 (916 obs)
##   Primary splits:
##       Transmitter splits as  ---------------------------------------L------------------------------------------L-----------------L-------------------------------------------------------L---------L------L-----L----------L---L-----------------------------L------------------------------------------------R-L----L---LL--L------------------------------------------L--R-L--------------------------------------------------L------------------------------RRRRLLRR-LLLL-R-R-L-RLRL--R-R--LRRRRRRL--LLRRLRLL--L-R---L--LR---L-LR-LRRLRL-L---R-L-----LLL--LL------------R-------LR---L-L--L--------LLL------------LL----LL--LLL------L------L--L----LL---LLL-L------LL------------------RL-L----LR--L-LRL---L---RL--LLL--R-LL--LR-RRL--L--L--R----LLRL-LL--------------------L------L---------R---L--L--L-------------------------------------------------------------L--L---L-------L-------------------LL-, improve=0.12322500, (0 missing)
##       File        splits as  -LLLRRLL-LLL-----RLRL-R-L-RRL-----R---LL--R-RL--, improve=0.05036611, (0 missing)
##       Species     splits as  RLRL--RLR--LLRR-R-L--R--LL-R--, improve=0.04419281, (0 missing)
##   Surrogate splits:
##       Species splits as  LLLL--RLR--LLRR-L-L--R--LL-L--, agree=0.789, adj=0.184, (0 split)
##       File    splits as  -LLLLLLL-LLL-----RLLL-R-L-RRL-----R---LL--L-LL--, agree=0.789, adj=0.184, (0 split)
## 
## Node number 4: 17166 observations
##   mean=0.1813689, MSE=0.00163798 
## 
## Node number 5: 4900 observations
##   mean=0.2508361, MSE=0.006851313 
## 
## Node number 6: 2630 observations
##   mean=0.3626308, MSE=0.03018033 
## 
## Node number 7: 916 observations,    complexity param=0.01662605
##   mean=0.5577085, MSE=0.1141737 
##   left son=14 (715 obs) right son=15 (201 obs)
##   Primary splits:
##       Transmitter splits as  ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------L------------------------------------------------------------L-----------------------------------------------------------------------------------LRRR--LL------R-R---R-L---R-L---RLRRLL-----LL-R------R-------R------L--LL-L------R--------------------------L--------R------------------------------------------------------------------------------------------------------L--------L-----L--------R--------L------R-RL---------L------R-----------------------------------------L-------------------------------------------------------------------------------------------------------------, improve=0.06879362, (0 missing)
##       Species     splits as  -LR---LLR--L-RL-R----L-----L--, improve=0.04110001, (0 missing)
##       File        splits as  --L-R------------LL---R---RLL-----L-------R-L---, improve=0.04110001, (0 missing)
##   Surrogate splits:
##       Species splits as  -LL---LLR--L-RL-R----L-----L--, agree=0.823, adj=0.194, (0 split)
##       File    splits as  --L-L------------LL---R---RLL-----L-------R-L---, agree=0.823, adj=0.194, (0 split)
## 
## Node number 14: 715 observations
##   mean=0.5107189, MSE=0.08078683 
## 
## Node number 15: 201 observations
##   mean=0.7248607, MSE=0.1971435

To choose which one of these variables include in the model, we tested all possible combinations.

glmmT_total1 <- glmmTMB(KUD50 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + ReceiverDensity + MonitArea_km2 + (1|Transmitter), data=week_kuds, family=Gamma(link="log"))

glmmF_total1 <- glmmTMB(KUD50 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + ReceiverDensity + MonitArea_km2 + (1|File), data=week_kuds, family=Gamma(link="log"))

glmmS_total1 <- glmmTMB(KUD50 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + ReceiverDensity + MonitArea_km2 + (1|Species), data=week_kuds, family=Gamma(link="log"))

glmmTF_total1 <- glmmTMB(KUD50 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + ReceiverDensity + MonitArea_km2 + (1|Transmitter) + (1|File), data=week_kuds, family=Gamma(link="log"))

glmmTS_total1 <- glmmTMB(KUD50 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + ReceiverDensity + MonitArea_km2 + (1|Transmitter) + (1|Species), data=week_kuds, family=Gamma(link="log"))

glmmFS_total1 <- glmmTMB(KUD50 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + ReceiverDensity + MonitArea_km2 + (1|File) + (1|Species), data=week_kuds, family=Gamma(link="log"))

glmmTFS_total1 <- glmmTMB(KUD50 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + ReceiverDensity + MonitArea_km2 + (1|Transmitter) + (1|File) + (1|Species), data=week_kuds, family=Gamma(link="log"))


AIC(glmmT_total1, glmmF_total1, glmmS_total1, glmmTF_total1, glmmTS_total1, glmmFS_total1, glmmTFS_total1)
##                df       AIC
## glmmT_total1   15 -76204.64
## glmmF_total1   15 -65971.34
## glmmS_total1   15 -63412.70
## glmmTF_total1  16 -76365.69
## glmmTS_total1  16 -76245.35
## glmmFS_total1  16 -65969.34
## glmmTFS_total1 17 -76363.69
#Now we need to compare if the models are statistically different or not
anova(glmmT_total1, glmmTF_total1) #significant differences, this two models differ statistically
## Data: week_kuds
## Models:
## glmmT_total1: KUD50 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmT_total1:     Troph + Habitat + Migration + ComImport + ReceiverDensity + , zi=~0, disp=~1
## glmmT_total1:     MonitArea_km2 + (1 | Transmitter), zi=~0, disp=~1
## glmmTF_total1: KUD50 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmTF_total1:     Troph + Habitat + Migration + ComImport + ReceiverDensity + , zi=~0, disp=~1
## glmmTF_total1:     MonitArea_km2 + (1 | Transmitter) + (1 | File), zi=~0, disp=~1
##               Df    AIC    BIC logLik deviance  Chisq Chi Df Pr(>Chisq)    
## glmmT_total1  15 -76205 -76082  38117   -76235                             
## glmmTF_total1 16 -76366 -76235  38199   -76398 163.05      1  < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(glmmT_total1, glmmTFS_total1) #significant differences, this two models differ statistically
## Data: week_kuds
## Models:
## glmmT_total1: KUD50 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmT_total1:     Troph + Habitat + Migration + ComImport + ReceiverDensity + , zi=~0, disp=~1
## glmmT_total1:     MonitArea_km2 + (1 | Transmitter), zi=~0, disp=~1
## glmmTFS_total1: KUD50 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmTFS_total1:     Troph + Habitat + Migration + ComImport + ReceiverDensity + , zi=~0, disp=~1
## glmmTFS_total1:     MonitArea_km2 + (1 | Transmitter) + (1 | File) + (1 | Species), zi=~0, disp=~1
##                Df    AIC    BIC logLik deviance  Chisq Chi Df Pr(>Chisq)    
## glmmT_total1   15 -76205 -76082  38117   -76235                             
## glmmTFS_total1 17 -76364 -76225  38199   -76398 163.05      2  < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(glmmTF_total1, glmmTFS_total1) #non significant differences, this two models do not differ statistically
## Data: week_kuds
## Models:
## glmmTF_total1: KUD50 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmTF_total1:     Troph + Habitat + Migration + ComImport + ReceiverDensity + , zi=~0, disp=~1
## glmmTF_total1:     MonitArea_km2 + (1 | Transmitter) + (1 | File), zi=~0, disp=~1
## glmmTFS_total1: KUD50 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmTFS_total1:     Troph + Habitat + Migration + ComImport + ReceiverDensity + , zi=~0, disp=~1
## glmmTFS_total1:     MonitArea_km2 + (1 | Transmitter) + (1 | File) + (1 | Species), zi=~0, disp=~1
##                Df    AIC    BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## glmmTF_total1  16 -76366 -76235  38199   -76398                        
## glmmTFS_total1 17 -76364 -76225  38199   -76398     0      1          1
anova(glmmTF_total1, glmmTS_total1) #non significant differences, this two models do not differ statistically
## Data: week_kuds
## Models:
## glmmTF_total1: KUD50 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmTF_total1:     Troph + Habitat + Migration + ComImport + ReceiverDensity + , zi=~0, disp=~1
## glmmTF_total1:     MonitArea_km2 + (1 | Transmitter) + (1 | File), zi=~0, disp=~1
## glmmTS_total1: KUD50 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmTS_total1:     Troph + Habitat + Migration + ComImport + ReceiverDensity + , zi=~0, disp=~1
## glmmTS_total1:     MonitArea_km2 + (1 | Transmitter) + (1 | Species), zi=~0, disp=~1
##               Df    AIC    BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## glmmTF_total1 16 -76366 -76235  38199   -76398                        
## glmmTS_total1 16 -76245 -76115  38139   -76277     0      0          1
anova(glmmTS_total1, glmmTFS_total1) #significant differences, this two models differ statistically
## Data: week_kuds
## Models:
## glmmTS_total1: KUD50 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmTS_total1:     Troph + Habitat + Migration + ComImport + ReceiverDensity + , zi=~0, disp=~1
## glmmTS_total1:     MonitArea_km2 + (1 | Transmitter) + (1 | Species), zi=~0, disp=~1
## glmmTFS_total1: KUD50 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + , zi=~0, disp=~1
## glmmTFS_total1:     Troph + Habitat + Migration + ComImport + ReceiverDensity + , zi=~0, disp=~1
## glmmTFS_total1:     MonitArea_km2 + (1 | Transmitter) + (1 | File) + (1 | Species), zi=~0, disp=~1
##                Df    AIC    BIC logLik deviance  Chisq Chi Df Pr(>Chisq)    
## glmmTS_total1  16 -76245 -76115  38139   -76277                             
## glmmTFS_total1 17 -76364 -76225  38199   -76398 120.33      1  < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#choose the simplest model with the lower AIC, that is statistically different from the others, which means the model with Transmitter and File as random effect (glmmTF_total1)

Having chosen the best variable to include in the model as random effect, we now must do a backward stepwise selection, to investigate which are the best predictor variables to explain the response behaviour.

Final1.1 <- glmmTMB(KUD50 ~ LengthStd + BodyMassStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + ReceiverDensity + MonitArea_km2 + (1|Transmitter) + (1|Species), data = week_kuds, family = Gamma(link="log"))
summary(Final1.1)
##  Family: Gamma  ( log )
## Formula:          
## KUD50 ~ LengthStd + BodyMassStd + Longevity + Vulnerability +  
##     Troph + Habitat + Migration + ComImport + ReceiverDensity +  
##     MonitArea_km2 + (1 | Transmitter) + (1 | Species)
## Data: week_kuds
## 
##      AIC      BIC   logLik deviance df.resid 
## -76245.4 -76114.9  38138.7 -76277.4    25596 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.07263  0.2695  
##  Species     (Intercept) 0.01513  0.1230  
## Number of obs: 25612, groups:  Transmitter, 850; Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0607 
## 
## Conditional model:
##                          Estimate Std. Error z value Pr(>|z|)    
## (Intercept)            -1.7157300  0.3143585  -5.458 4.82e-08 ***
## LengthStd               0.2458545  0.1432018   1.717   0.0860 .  
## BodyMassStd            -0.0223783  0.1028729  -0.218   0.8278    
## Longevity              -0.0023740  0.0030164  -0.787   0.4313    
## Vulnerability          -0.0051743  0.0037398  -1.384   0.1665    
## Troph                   0.0901465  0.0958326   0.941   0.3469    
## Habitatdemersal        -0.0783890  0.0880878  -0.890   0.3735    
## Habitatpelagic-neritic  0.2831266  0.1373777   2.061   0.0393 *  
## Migrationoceanodromous  0.0953280  0.1094855   0.871   0.3839    
## ComImportmedium        -0.1393933  0.0631082  -2.209   0.0272 *  
## ComImportminor         -0.2268463  0.1011143  -2.243   0.0249 *  
## ReceiverDensity         0.0002267  0.0005662   0.400   0.6889    
## MonitArea_km2           0.0225680  0.0033794   6.678 2.42e-11 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Final2.1 <- glmmTMB(KUD50 ~ LengthStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + ReceiverDensity + MonitArea_km2 + (1|Transmitter) + (1|Species), data = week_kuds, family = Gamma(link="log"))
summary(Final2.1)
##  Family: Gamma  ( log )
## Formula:          
## KUD50 ~ LengthStd + Longevity + Vulnerability + Troph + Habitat +  
##     Migration + ComImport + ReceiverDensity + MonitArea_km2 +  
##     (1 | Transmitter) + (1 | Species)
## Data: week_kuds
## 
##      AIC      BIC   logLik deviance df.resid 
## -76247.3 -76125.0  38138.7 -76277.3    25597 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.07262  0.2695  
##  Species     (Intercept) 0.01530  0.1237  
## Number of obs: 25612, groups:  Transmitter, 850; Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0607 
## 
## Conditional model:
##                          Estimate Std. Error z value Pr(>|z|)    
## (Intercept)            -1.7193718  0.3151555  -5.456 4.88e-08 ***
## LengthStd               0.2235444  0.0997660   2.241   0.0250 *  
## Longevity              -0.0023805  0.0030303  -0.786   0.4321    
## Vulnerability          -0.0050875  0.0037327  -1.363   0.1729    
## Troph                   0.0907949  0.0961680   0.944   0.3451    
## Habitatdemersal        -0.0743238  0.0864098  -0.860   0.3897    
## Habitatpelagic-neritic  0.2842390  0.1378535   2.062   0.0392 *  
## Migrationoceanodromous  0.0968352  0.1097434   0.882   0.3776    
## ComImportmedium        -0.1414672  0.0626290  -2.259   0.0239 *  
## ComImportminor         -0.2304849  0.1000788  -2.303   0.0213 *  
## ReceiverDensity         0.0002373  0.0005648   0.420   0.6744    
## MonitArea_km2           0.0226918  0.0033333   6.808 9.92e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Final3.1 <- glmmTMB(KUD50 ~ LengthStd + Longevity + Vulnerability + Troph + Habitat + Migration + ComImport + MonitArea_km2 + (1|Transmitter) + (1|Species), data = week_kuds, family = Gamma(link="log"))
summary(Final3.1)
##  Family: Gamma  ( log )
## Formula:          
## KUD50 ~ LengthStd + Longevity + Vulnerability + Troph + Habitat +  
##     Migration + ComImport + MonitArea_km2 + (1 | Transmitter) +  
##     (1 | Species)
## Data: week_kuds
## 
##      AIC      BIC   logLik deviance df.resid 
## -76249.1 -76135.0  38138.6 -76277.1    25598 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.07267  0.2696  
##  Species     (Intercept) 0.01535  0.1239  
## Number of obs: 25612, groups:  Transmitter, 850; Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0607 
## 
## Conditional model:
##                         Estimate Std. Error z value Pr(>|z|)    
## (Intercept)            -1.723886   0.315364  -5.466 4.59e-08 ***
## LengthStd               0.222754   0.099682   2.235   0.0254 *  
## Longevity              -0.002370   0.003034  -0.781   0.4347    
## Vulnerability          -0.005199   0.003729  -1.394   0.1633    
## Troph                   0.096182   0.095435   1.008   0.3135    
## Habitatdemersal        -0.074140   0.086525  -0.857   0.3915    
## Habitatpelagic-neritic  0.275118   0.136285   2.019   0.0435 *  
## Migrationoceanodromous  0.101036   0.109428   0.923   0.3558    
## ComImportmedium        -0.139394   0.062532  -2.229   0.0258 *  
## ComImportminor         -0.229239   0.100178  -2.288   0.0221 *  
## MonitArea_km2           0.021836   0.002643   8.262  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Final4.1 <- glmmTMB(KUD50 ~ LengthStd + Vulnerability + Troph + Habitat + Migration + ComImport + MonitArea_km2 + (1|Transmitter) + (1|Species), data = week_kuds, family = Gamma(link="log"))
summary(Final4.1)
##  Family: Gamma  ( log )
## Formula:          
## KUD50 ~ LengthStd + Vulnerability + Troph + Habitat + Migration +  
##     ComImport + MonitArea_km2 + (1 | Transmitter) + (1 | Species)
## Data: week_kuds
## 
##      AIC      BIC   logLik deviance df.resid 
## -76250.5 -76144.6  38138.3 -76276.5    25599 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.07267  0.2696  
##  Species     (Intercept) 0.01588  0.1260  
## Number of obs: 25612, groups:  Transmitter, 850; Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0607 
## 
## Conditional model:
##                         Estimate Std. Error z value Pr(>|z|)    
## (Intercept)            -1.668849   0.311159  -5.363 8.17e-08 ***
## LengthStd               0.228970   0.099385   2.304  0.02123 *  
## Vulnerability          -0.006287   0.003483  -1.805  0.07101 .  
## Troph                   0.083785   0.095444   0.878  0.38002    
## Habitatdemersal        -0.076708   0.087434  -0.877  0.38031    
## Habitatpelagic-neritic  0.326777   0.121959   2.679  0.00738 ** 
## Migrationoceanodromous  0.093105   0.110542   0.842  0.39965    
## ComImportmedium        -0.140498   0.063356  -2.218  0.02658 *  
## ComImportminor         -0.227251   0.101329  -2.243  0.02492 *  
## MonitArea_km2           0.021585   0.002626   8.219  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Final5.1 <- glmmTMB(KUD50 ~ LengthStd + Vulnerability + Troph + Habitat + ComImport + MonitArea_km2 + (1|Transmitter) + (1|Species), data = week_kuds, family = Gamma(link="log"))
summary(Final5.1)
##  Family: Gamma  ( log )
## Formula:          
## KUD50 ~ LengthStd + Vulnerability + Troph + Habitat + ComImport +  
##     MonitArea_km2 + (1 | Transmitter) + (1 | Species)
## Data: week_kuds
## 
##      AIC      BIC   logLik deviance df.resid 
## -76251.8 -76154.0  38137.9 -76275.8    25600 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.07264  0.2695  
##  Species     (Intercept) 0.01690  0.1300  
## Number of obs: 25612, groups:  Transmitter, 850; Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0607 
## 
## Conditional model:
##                         Estimate Std. Error z value Pr(>|z|)    
## (Intercept)            -1.606929   0.309045  -5.200 2.00e-07 ***
## LengthStd               0.221855   0.099357   2.233 0.025556 *  
## Vulnerability          -0.004912   0.003168  -1.551 0.121004    
## Troph                   0.058957   0.093102   0.633 0.526568    
## Habitatdemersal        -0.120749   0.071785  -1.682 0.092549 .  
## Habitatpelagic-neritic  0.381715   0.106803   3.574 0.000352 ***
## ComImportmedium        -0.138028   0.064823  -2.129 0.033228 *  
## ComImportminor         -0.208862   0.101139  -2.065 0.038914 *  
## MonitArea_km2           0.021511   0.002632   8.173 3.01e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Final6.1 <- glmmTMB(KUD50 ~ LengthStd + Vulnerability + Habitat + ComImport + MonitArea_km2 + (1|Transmitter) + (1|Species), data = week_kuds, family = Gamma(link="log"))
summary(Final6.1)
##  Family: Gamma  ( log )
## Formula:          
## KUD50 ~ LengthStd + Vulnerability + Habitat + ComImport + MonitArea_km2 +  
##     (1 | Transmitter) + (1 | Species)
## Data: week_kuds
## 
##      AIC      BIC   logLik deviance df.resid 
## -76253.4 -76163.8  38137.7 -76275.4    25601 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.07257  0.2694  
##  Species     (Intercept) 0.01749  0.1322  
## Number of obs: 25612, groups:  Transmitter, 850; Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0607 
## 
## Conditional model:
##                         Estimate Std. Error z value Pr(>|z|)    
## (Intercept)            -1.449973   0.183845  -7.887 3.10e-15 ***
## LengthStd               0.219014   0.099391   2.204   0.0276 *  
## Vulnerability          -0.003803   0.002688  -1.415   0.1572    
## Habitatdemersal        -0.124199   0.072500  -1.713   0.0867 .  
## Habitatpelagic-neritic  0.414891   0.095007   4.367 1.26e-05 ***
## ComImportmedium        -0.135728   0.065596  -2.069   0.0385 *  
## ComImportminor         -0.192866   0.099180  -1.945   0.0518 .  
## MonitArea_km2           0.021573   0.002634   8.191 2.58e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Final7.1 <- glmmTMB(KUD50 ~ LengthStd  + Habitat + ComImport + MonitArea_km2 + (1|Transmitter) + (1|Species), data = week_kuds, family = Gamma(link="log"))
summary(Final7.1)
##  Family: Gamma  ( log )
## Formula:          
## KUD50 ~ LengthStd + Habitat + ComImport + MonitArea_km2 + (1 |  
##     Transmitter) + (1 | Species)
## Data: week_kuds
## 
##      AIC      BIC   logLik deviance df.resid 
## -76253.6 -76172.1  38136.8 -76273.6    25602 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.07240  0.2691  
##  Species     (Intercept) 0.02014  0.1419  
## Number of obs: 25612, groups:  Transmitter, 850; Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0607 
## 
## Conditional model:
##                         Estimate Std. Error z value Pr(>|z|)    
## (Intercept)            -1.689221   0.075732 -22.305  < 2e-16 ***
## LengthStd               0.219283   0.100006   2.193   0.0283 *  
## Habitatdemersal        -0.104884   0.074901  -1.400   0.1614    
## Habitatpelagic-neritic  0.399792   0.098870   4.044 5.26e-05 ***
## ComImportmedium        -0.134535   0.069372  -1.939   0.0525 .  
## ComImportminor         -0.128633   0.092506  -1.391   0.1644    
## MonitArea_km2           0.022167   0.002619   8.464  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Final8.1 <- glmmTMB(KUD50 ~ LengthStd  + Habitat + MonitArea_km2 + (1|Transmitter) + (1|Species), data = week_kuds, family = Gamma(link="log"))
summary(Final8.1)
##  Family: Gamma  ( log )
## Formula:          
## KUD50 ~ LengthStd + Habitat + MonitArea_km2 + (1 | Transmitter) +  
##     (1 | Species)
## Data: week_kuds
## 
##      AIC      BIC   logLik deviance df.resid 
## -76253.6 -76188.4  38134.8 -76269.6    25604 
## 
## Random effects:
## 
## Conditional model:
##  Groups      Name        Variance Std.Dev.
##  Transmitter (Intercept) 0.07236  0.2690  
##  Species     (Intercept) 0.02482  0.1575  
## Number of obs: 25612, groups:  Transmitter, 850; Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0607 
## 
## Conditional model:
##                        Estimate Std. Error z value Pr(>|z|)    
## (Intercept)            -1.75314    0.07420 -23.626  < 2e-16 ***
## LengthStd               0.21974    0.09990   2.199 0.027846 *  
## Habitatdemersal        -0.12543    0.07637  -1.642 0.100496    
## Habitatpelagic-neritic  0.36534    0.10438   3.500 0.000465 ***
## MonitArea_km2           0.02219    0.00265   8.374  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(Final7.1, Final8.1) #as there is no evidence that this two models are statistically different from each other, we must choose the simplest one, the one with less variables (Final7)
## Data: week_kuds
## Models:
## Final8.1: KUD50 ~ LengthStd + Habitat + MonitArea_km2 + (1 | Transmitter) + , zi=~0, disp=~1
## Final8.1:     (1 | Species), zi=~0, disp=~1
## Final7.1: KUD50 ~ LengthStd + Habitat + ComImport + MonitArea_km2 + (1 | , zi=~0, disp=~1
## Final7.1:     Transmitter) + (1 | Species), zi=~0, disp=~1
##          Df    AIC    BIC logLik deviance  Chisq Chi Df Pr(>Chisq)
## Final8.1  8 -76254 -76188  38135   -76270                         
## Final7.1 10 -76254 -76172  38137   -76274 4.0146      2     0.1343
ranef(Final8.1) #deviation from intercept for each Transmitter and File group
## $Transmitter
##                  (Intercept)
## 1               0.0235557416
## 10              0.2526024228
## 11             -0.0236819004
## 12             -0.1414346222
## 123             0.2364263128
## 1248320         0.0607184115
## 1248321         0.0673424504
## 1248322         0.0604500017
## 1248323         0.0789799191
## 1248324         0.0500901662
## 1248325         0.0811944258
## 1248326         0.0898321624
## 1248327         0.0626080031
## 1248328         0.0827558956
## 1248329         0.0735479147
## 1248330         0.0869992729
## 1248331         0.0906463290
## 1248332         0.0803278996
## 1248333         0.1161966148
## 1248334         0.0517147996
## 1248335         0.0799249937
## 13             -0.0866992079
## 131             0.2540335157
## 14              0.0899330884
## 15              0.1747265787
## 1511           -0.1511315847
## 1512           -0.2204445736
## 1513           -0.0635954881
## 1516            0.0694904621
## 1517            0.1912122336
## 1519           -0.0996389664
## 1521            0.1228062957
## 1522            0.2519082288
## 1523            0.0799950187
## 1524            0.0463376672
## 1527            0.1832352145
## 1528           -0.2440627947
## 1530            0.1119243839
## 1531            0.1183838670
## 1532            0.3395661916
## 1533           -0.0746119045
## 1534           -0.1100499627
## 1536            0.0619882603
## 1537           -0.1488972407
## 1538           -0.2137410940
## 1539           -0.1575099356
## 1540            0.2644615155
## 1541           -0.1385076872
## 1542           -0.0241669303
## 1543            0.2741131328
## 1544            0.2042512733
## 1545           -0.1290994420
## 1546           -0.0591701647
## 1547           -0.0960747548
## 1549           -0.0841455137
## 1551            0.0708087403
## 1552            0.0786676281
## 1553           -0.2041595594
## 1554           -0.0764792220
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## 1565           -0.1795779364
## 1566           -0.0617107522
## 1568           -0.1873723011
## 1569            0.1008720737
## 1570            0.1511576512
## 1571            0.0401888234
## 1574            0.1296353717
## 1575            0.2537014726
## 1576           -0.1116057674
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## 16             -0.0846848386
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## 1618           -0.2474196309
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## 1624           -0.1302016138
## 17              0.1136978544
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## 1917           -0.3047313829
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## 1933            0.2045511989
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## 1953           -0.0686738503
## 1954           -0.1072871462
## 1955           -0.2802300661
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## 1958            0.2105071983
## 1964           -0.2584616170
## 1966           -0.1524026181
## 1968           -0.3041191927
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## 1991           -0.3156134699
## 1992            0.5354717591
## 1994           -0.0427774360
## 2               0.1799919812
## 20              0.1482851661
## 21             -0.0762846967
## 22              0.1312109249
## 220            -0.1326732689
## 23             -0.0688107598
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## 241             0.0329091430
## 25              0.4153821559
## 26              0.0383715959
## 27             -0.0595945283
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## 3               0.0624253699
## 30              0.1950974391
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## 7               0.1479476440
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## A69-1008-1     -0.2157233889
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## A69-1602-40690  0.4434446825
## A69-1602-40692  0.0768899154
## A69-1602-40693  0.0155993631
## A69-1602-40694 -0.0437911685
## A69-1602-40695  0.0871309618
## A69-1602-40697  0.3653486368
## A69-1602-40699  0.5154103471
## A69-1602-40700  0.3811149927
## A69-1602-40701  0.9026188567
## A69-1602-40702  0.4419246033
## A69-1602-40703  0.2748826812
## A69-1602-40704 -0.2051258476
## A69-1602-40705 -0.2228533695
## A69-1602-40706  0.5065768927
## A69-1602-40707 -0.2441022865
## A69-1602-40708  1.0817368964
## A69-1602-40709  0.0986157065
## A69-1602-40710 -0.3725029867
## A69-1602-40712 -0.0300232274
## A69-1602-40716  0.1017325483
## A69-1602-40717 -0.1845218516
## A69-1602-40718 -0.2882073252
## A69-1602-40719  0.1913562393
## A69-1602-40721  1.2571455235
## A69-1602-40722 -0.4782630898
## A69-1602-40725 -0.2938731113
## A69-1602-40726 -0.3603548161
## A69-1602-40727  0.1947773391
## A69-1602-40728  0.0165216038
## A69-1602-40729  0.0378439124
## A69-1602-40730  0.4340390533
## A69-1602-40731 -0.5385956518
## A69-1602-40732  0.2907668528
## A69-1602-40736  0.3778344794
## A69-1602-40737  0.3363726455
## A69-1602-40738  0.0350583731
## A69-1602-54170  0.6337100173
## A69-1602-54171  0.2496806826
## A69-1602-54172 -0.4720397755
## A69-1602-54173  0.1667645063
## A69-1602-54174 -0.1479103165
## A69-9004-415   -0.4473019651
## A69-9006-11423 -0.2755635831
## A69-9006-11425  0.5742973813
## A69-9006-11426 -0.6461352731
## A69-9006-11428 -0.0314183412
## A69-9006-11430 -0.4490053008
## A69-9006-11437 -0.0936355813
## A69-9006-11444 -0.1079524610
## A69-9006-11445 -0.1479409111
## A69-9006-11446 -0.6582699663
## A69-9006-11447 -0.2393402025
## A69-9006-11448  0.0768841763
## A69-9006-11449 -0.0121023328
## A69-9006-11450 -0.5074103167
## A69-9006-11484 -0.2257985998
## A69-9006-11492 -0.0336019377
## A69-9006-11494  0.1837585805
## A69-9006-11496 -0.1406853249
## A69-9006-11498 -0.1792610414
## A69-9006-11522 -0.2640031076
## A69-9006-11526 -0.1649201664
## A69-9006-11528 -0.2730870873
## A69-9006-11532 -0.2383989465
## A69-9006-11534 -0.1874064201
## A69-9006-11536 -0.1572321047
## A69-9006-11538 -0.2230737096
## A69-9006-11540 -0.2320203394
## A69-9006-11542 -0.1872310368
## A69-9006-11546  0.1523046712
## A69-9006-11548  0.6683612520
## A69-9006-11550  0.1703649683
## A69-9006-11552  0.2543570704
## A69-9006-11554 -0.0755816919
## A69-9006-11556 -0.2788297901
## A69-9006-11558 -0.1921174824
## A69-9006-11560 -0.2294047040
## A69-9006-11573 -0.4305551447
## A69-9006-11574  0.1186276365
## A69-9006-11575  0.8020774648
## A69-9006-11577 -0.2462355701
## A69-9006-11578 -0.1575139092
## A69-9006-11579 -0.3267174867
## A69-9006-11581  0.1730640809
## A69-9006-11582  0.0292379702
## A69-9006-11583  0.2256809810
## A69-9006-11587 -0.0455254301
## A69-9006-11588 -0.2448720523
## A69-9006-11591  0.0290390972
## A69-9006-3642  -0.4151650580
## A69-9006-3643  -0.4077209549
## A69-9006-3644   0.0795937431
## A69-9006-3645   0.0763415269
## A69-9006-3650  -0.3840463387
## A69-9006-3651  -0.3404390258
## A69-9006-3653  -0.2249915476
## A69-9006-3654   0.0552581628
## A69-9006-3655   0.2084357619
## A69-9006-3658   0.4424706964
## A69-9006-3659   0.5061166938
## A69-9006-3660  -0.3098637927
## A69-9006-3661  -0.3098637927
## A69-9006-3664  -0.3190495806
## A69-9006-3665  -0.3194134896
## A69-9006-3666  -0.4333876339
## A69-9006-3667  -0.4497231137
## A69-9006-3668  -0.3170928591
## A69-9006-3669  -0.3300273764
## A69-9006-3672  -0.1919069821
## A69-9006-3673  -0.2253781410
## A69-9006-3674  -0.3020473289
## A69-9006-3675  -0.3020473289
## A69-9006-3678   0.1436532282
## A69-9006-3679   0.1866001078
## A69-9006-3680  -0.3588961070
## A69-9006-3681  -0.3637694049
## A69-9006-3682   0.0363734530
## A69-9006-3683   0.0515587618
## A69-9006-3684   0.2133586215
## A69-9006-3685   0.1503297357
## A69-9006-3686  -0.3250647925
## A69-9006-3687  -0.3844825199
## A69-9006-3690   0.4464653925
## A69-9006-3691   0.4831634267
## A69-9006-3692   0.4821579744
## A69-9006-3693   0.0890024981
## A69-9006-3694  -0.4271113106
## A69-9006-3695  -0.4200026242
## A69-9006-3698  -0.3032840556
## A69-9006-3699  -0.3796331838
## A69-9006-3700   0.0198662792
## A69-9006-3701   0.1327142683
## A69-9006-3702  -0.1440058752
## A69-9006-3703   0.1173831857
## A69-9006-3704  -0.4493241174
## A69-9006-3705  -0.4501054008
## A69-9006-3706  -0.3538430807
## A69-9006-3707  -0.3460116342
## A69-9006-3712   0.1916052845
## A69-9006-3713   0.1469357222
## A69-9006-3716  -0.0470664071
## A69-9006-3717   0.3701285211
## A69-9006-3734  -0.0080528312
## A69-9006-3735  -0.0152503458
## A69-9006-3736  -0.3353281477
## A69-9006-3737  -0.3258796865
## A69-9006-3738   0.2493667528
## A69-9006-3739   0.2768328985
## A69-9006-3740  -0.3599352216
## A69-9006-3741  -0.2152613628
## A69-9006-3742   0.0851869211
## A69-9006-3743   0.1500497278
## A69-9006-3746   0.1096738614
## A69-9006-3747   0.1275602000
## A69-9006-3748   0.0523379473
## A69-9006-3749   0.1548888534
## A69-9006-3750  -0.2985907676
## A69-9006-3751  -0.2224117695
## A69-9006-3752  -0.0988001268
## A69-9006-3753  -0.1438406636
## A69-9006-921   -0.2203122193
## A69-9006-922   -0.2521003956
## A69-9006-923    0.3029324141
## A69-9006-924    0.2753032588
## A69-9006-929   -0.2837919707
## A69-9006-930   -0.2848636540
## A69-9006-931   -0.3851786229
## A69-9006-932   -0.3822831044
## A69-9006-933    0.0592365506
## A69-9006-934    0.0317961766
## A69-9006-935   -0.3105844349
## A69-9006-936   -0.3136722768
## A69-9006-941   -0.3604325326
## A69-9006-942   -0.3604325326
## A69-9006-943   -0.1465288576
## A69-9006-944   -0.1334915890
## A69-9006-945    0.0274502711
## A69-9006-946    0.0161937579
## A69-9006-951   -0.0767781074
## A69-9006-952   -0.0549625993
## A69-9006-987    0.0556985746
## A69-9006-988    0.0556985746
## A69-9007-16169  0.3056930988
## A69-9007-16170 -0.0971619796
## A69-9007-16172 -0.4991598128
## A69-9007-16173  0.0954208655
## A69-9007-16174 -0.3777995527
## A69-9007-16176 -0.2724593140
## A69-9007-16177 -0.0191459822
## A69-9007-16182 -0.2002615991
## A69-9007-16184  0.2270455504
## A69-9007-16185  0.1597104390
## A69-9007-16187 -0.5380081511
## A69-9007-16190 -0.1969605218
## A69-9007-16199  0.2090160024
## A69-9007-16202  0.0514465713
## A69-9007-16204  0.3647952833
## A69-9007-16205  0.1887607937
## A69-9007-16206  0.1604723762
## A69-9007-16207 -0.2612745810
## A69-9007-16208 -0.4924935992
## A69-9007-16209  0.2124893949
## A69-9007-16210 -0.1534497236
## A69-9007-16212 -0.5084904664
## A69-9007-16213 -0.2991041394
## A69-9007-16214 -0.5007730831
## A69-9007-16215  0.8371651632
## A69-9007-16217  0.2205480727
## A69-9007-16218 -0.1911313408
## A69-9007-16219  0.1796445524
## A69-9007-16220  0.0701797496
## A69-9007-16221  0.2015765981
## A69-9007-16222  0.0037131774
## A69-9007-16223 -0.2853467275
## A69-9007-16224 -0.3314691861
## A69-9007-16225  0.7413838189
## A69-9007-16226  0.2511495777
## A69-9007-16227  0.4579630777
## A69-9007-16228  0.2104377252
## A69-9007-16229 -0.1849676084
## A69-9007-16230 -0.6221435975
## A69-9007-16231  0.2830469532
## A69-9007-16233  0.4551038362
## A69-9007-16234 -0.3410603002
## A69-9007-16235  0.4013868365
## A69-9007-16241  0.3546442286
## A69-9007-16243  0.0534013421
## A69-9007-16245 -0.0886838029
## A69-9007-16247 -0.1885425306
## A69-9007-16252  0.3817567273
## A69-9007-16253 -0.0387356171
## A69-9007-16254 -0.0823067774
## A69-9007-16255  0.4779689463
## A69-9007-16256 -0.0473705130
## A69-9007-16257 -0.2464099305
## A69-9007-16258  0.4874385363
## A69-9007-16259 -0.1680373681
## A69-9007-16260 -0.1432279109
## A69-9007-16261 -0.6098258756
## A69-9007-16262 -0.1229028854
## A69-9007-16263  0.1436379675
## A69-9007-16264  0.1985089926
## A69-9007-16265  0.7898654028
## A69-9007-16266  0.0710775336
## A69-9007-16267 -0.2830218552
## A69-9007-16269  0.1747015045
## A69-9007-16270  0.3205397610
## A69-9007-16272 -0.0608084371
## A69-9007-16273 -0.1998178696
## A69-9007-16274 -0.1956389594
## A9001          -0.0896584217
## A9002           0.0831563738
## A9004          -0.0632843531
## A9006           0.0006717447
## A9007          -0.1685841308
## A9008           0.2572777530
## A9009           0.1038005734
## A9010           0.3023419040
## A9011           0.0981064873
## A9012           0.1235058658
## A9013          -0.1094341623
## A9014           0.3533149632
## A9015          -0.0799345821
## A9016          -0.0039316256
## A9017           0.1456641796
## A9018          -0.1014457509
## A9019           0.1478889761
## A9020           0.3968100079
## A9021           0.1490684144
## A9023           0.2102586726
## A9025          -0.0490174837
## A9026          -0.0525623358
## A9027          -0.1557860572
## A9028           0.3357362354
## A9030           0.4853167972
## A9031           0.0245475932
## A9032           0.1922246526
## A9033           0.1443446341
## A9034          -0.0702299465
## A9035           0.1798616834
## A9036          -0.1688908039
## A9039           0.0268469272
## A9040           0.2829274330
## A9041          -0.0494100215
## A9042           0.8264157376
## A9043           0.3886841049
## A9045           0.0016223987
## A9046           0.0899292890
## A9047           0.4316329745
## A9048           0.3932723363
## A9049           0.1157203801
## A9050           0.5354536499
## A9051           0.3469529553
## A9052           0.1254431883
## A9054           0.5202306204
## A9055           0.1625334022
## A9056           0.0487160626
## A9057           0.2299584744
## A9058           0.0447701928
## A9059           0.0957652064
## A9060          -0.0832394764
## A9061          -0.0613990341
## A9063           0.3652252869
## A9065          -0.1562885102
## A9071           0.0431425730
## A9076          -0.0577910970
## A9083           0.0990820643
## A9087          -0.0019923667
## A9096          -0.0083642676
## A9100          -0.1645626722
## G1              0.0998229548
## G2              0.0305756890
## G3              0.0889422332
## G4              0.1209037760
## ID_18965        0.0006553784
## ID_18967       -0.0518271684
## ID_18971       -0.0332908123
## ID_18972       -0.3184556143
## ID_18976       -0.2257791269
## ID_18977       -0.1099480401
## ID_18978       -0.0997398967
## ID_18979       -0.2928026312
## ID_18982        0.0188168423
## ID_18983a      -0.0466263110
## ID_18984       -0.0286174424
## ID_18985       -0.0017839354
## ID_19051       -0.0862923372
## ID_19052       -0.0693313926
## ID_19053       -0.2248924415
## ID_19054       -0.3011383119
## ID_19055       -0.2286116850
## ID_19057       -0.2313082810
## ID_19059       -0.1095656696
## ID_9839         0.0274346137
## ID_9840        -0.0817477869
## ID_9841         0.1886240187
## ID_9842         0.0943138545
## ID_9843         0.1300819329
## ID_9845         0.1013314185
## ID_9846         0.1602455445
## ID_9847        -0.0033895033
## ID_9849         0.0192700769
## ID_9850        -0.0348094728
## ID_9851         0.1597198145
## ID_9852         0.1963401501
## ID_9853        -0.0400380010
## ID_9854         0.0516321708
## ID_9856        -0.0316883280
## ID_9857         0.0089504002
## ID_9859        -0.0142858282
## ID_9860         0.0003520801
## ID_9861         0.0606216448
## ID_9862         0.1743834897
## ID_9864         0.1002516661
## ID_9865         0.0341423695
## ID_9866         0.0533254177
## Linguado 01     0.3668356237
## Linguado 02    -0.1782706404
## Linguado 03    -0.2359291102
## Linguado 04     0.2739238274
## Linguado 05    -0.0102951769
## Linguado 06    -0.1445957135
## Linguado 07     0.1690947439
## Linguado 08     0.3130936200
## Linguado 09    -0.0728804166
## Linguado 10     0.0557213779
## Linguado 11    -0.1481564249
## Linguado 12    -0.1100527746
## Linguado 13    -0.2465392286
## Linguado 14    -0.2547944344
## Linguado 15     0.2139282020
## Linguado 16     0.2925400663
## Linguado 17    -0.1530103056
## Linguado 18     0.0540638023
## Linguado 19     0.0227366321
## Linguado 20    -0.0598711807
## Linguado 21     0.0515351924
## Linguado 22     0.2587324262
## Sargo 02        0.2987199398
## Sargo 03       -0.1608950500
## Sargo 05        0.0627491135
## Sargo 06        0.0067259439
## Sargo 07       -0.2030246625
## Sargo 08        0.2976537049
## Sargo 09       -0.1425130326
## Sargo 12       -0.1448464659
## Sargo 13       -0.0924440266
## Sargo 14       -0.1207699793
## Sargo 15       -0.0990747929
## Sargo 16       -0.1012388748
## Sargo 17        0.0475526437
## Sargo 18        0.5171973121
## Sargo 19        0.5596125179
## Sargo 20       -0.1492651472
## 
## $Species
##        (Intercept)
## Dcer  0.0009498575
## Dden  0.1538587164
## Dlab  0.0135768003
## Dsar  0.1009528683
## Dvol -0.0321196249
## Dvul  0.1538572173
## Emar -0.1197973105
## Gmor  0.0480672545
## Lami  0.1561133527
## Lber  0.0186945463
## Lmor -0.0532667172
## Pden -0.0094406964
## Pery  0.0940015630
## Ppag -0.0738995156
## Psal  0.1909531287
## Satr -0.2010077388
## Saur  0.3023546747
## Scab  0.1101643372
## Scan -0.1592233856
## Scre -0.1090916370
## Scro -0.1598848699
## Sdum  0.1363205164
## Spor  0.0076616124
## Sriv -0.3091690019
## Sscr -0.1649789959
## Ssen  0.1570416797
## Sumb -0.0599962125
## Svir -0.1785783486
## Ucir -0.0675629713
## Xnov -0.0190072629
confint(Final8.1)
##                                       2.5 %      97.5 %    Estimate
## (Intercept)                     -1.89857595 -1.60770012 -1.75313804
## LengthStd                        0.02392617  0.41554500  0.21973559
## Habitatdemersal                 -0.27511895  0.02424848 -0.12543523
## Habitatpelagic-neritic           0.16076388  0.56992459  0.36534423
## MonitArea_km2                    0.01699665  0.02738485  0.02219075
## Std.Dev.(Intercept)|Transmitter  0.25465676  0.28413786  0.26899373
## Std.Dev.(Intercept)|Species      0.11299537  0.21966114  0.15754584
#evaluate if the final model is better than the ones including only one biological trait
anova(Final8.1, glmmTMB(KUD50 ~ LengthStd + (1|Transmitter) + (1|Species), data=week_kuds, family=Gamma(link="log")))
## Data: week_kuds
## Models:
## glmmTMB(KUD50 ~ LengthStd + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log")): KUD50 ~ LengthStd + (1 | Transmitter) + (1 | Species), zi=~0, disp=~1
## Final8.1: KUD50 ~ LengthStd + Habitat + MonitArea_km2 + (1 | Transmitter) + , zi=~0, disp=~1
## Final8.1:     (1 | Species), zi=~0, disp=~1
##                                                                                                                Df
## glmmTMB(KUD50 ~ LengthStd + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log"))  5
## Final8.1                                                                                                        8
##                                                                                                                   AIC
## glmmTMB(KUD50 ~ LengthStd + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log")) -76174
## Final8.1                                                                                                       -76254
##                                                                                                                   BIC
## glmmTMB(KUD50 ~ LengthStd + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log")) -76133
## Final8.1                                                                                                       -76188
##                                                                                                                logLik
## glmmTMB(KUD50 ~ LengthStd + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log"))  38092
## Final8.1                                                                                                        38135
##                                                                                                                deviance
## glmmTMB(KUD50 ~ LengthStd + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log"))   -76184
## Final8.1                                                                                                         -76270
##                                                                                                                 Chisq
## glmmTMB(KUD50 ~ LengthStd + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log"))       
## Final8.1                                                                                                       85.866
##                                                                                                                Chi Df
## glmmTMB(KUD50 ~ LengthStd + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log"))       
## Final8.1                                                                                                            3
##                                                                                                                Pr(>Chisq)
## glmmTMB(KUD50 ~ LengthStd + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log"))           
## Final8.1                                                                                                        < 2.2e-16
##                                                                                                                   
## glmmTMB(KUD50 ~ LengthStd + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log"))    
## Final8.1                                                                                                       ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(Final8.1, glmmTMB(KUD50 ~ BodyMassStd + (1|Transmitter) + (1|Species), data=week_kuds, family=Gamma(link="log")))
## Data: week_kuds
## Models:
## glmmTMB(KUD50 ~ BodyMassStd + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log")): KUD50 ~ BodyMassStd + (1 | Transmitter) + (1 | Species), zi=~0, disp=~1
## Final8.1: KUD50 ~ LengthStd + Habitat + MonitArea_km2 + (1 | Transmitter) + , zi=~0, disp=~1
## Final8.1:     (1 | Species), zi=~0, disp=~1
##                                                                                                                  Df
## glmmTMB(KUD50 ~ BodyMassStd + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log"))  5
## Final8.1                                                                                                          8
##                                                                                                                     AIC
## glmmTMB(KUD50 ~ BodyMassStd + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log")) -76166
## Final8.1                                                                                                         -76254
##                                                                                                                     BIC
## glmmTMB(KUD50 ~ BodyMassStd + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log")) -76125
## Final8.1                                                                                                         -76188
##                                                                                                                  logLik
## glmmTMB(KUD50 ~ BodyMassStd + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log"))  38088
## Final8.1                                                                                                          38135
##                                                                                                                  deviance
## glmmTMB(KUD50 ~ BodyMassStd + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log"))   -76176
## Final8.1                                                                                                           -76270
##                                                                                                                   Chisq
## glmmTMB(KUD50 ~ BodyMassStd + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log"))       
## Final8.1                                                                                                         93.868
##                                                                                                                  Chi Df
## glmmTMB(KUD50 ~ BodyMassStd + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log"))       
## Final8.1                                                                                                              3
##                                                                                                                  Pr(>Chisq)
## glmmTMB(KUD50 ~ BodyMassStd + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log"))           
## Final8.1                                                                                                          < 2.2e-16
##                                                                                                                     
## glmmTMB(KUD50 ~ BodyMassStd + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log"))    
## Final8.1                                                                                                         ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(Final8.1, glmmTMB(KUD50 ~ Habitat + (1|Transmitter) + (1|Species), data=week_kuds, family=Gamma(link="log")))
## Data: week_kuds
## Models:
## glmmTMB(KUD50 ~ Habitat + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log")): KUD50 ~ Habitat + (1 | Transmitter) + (1 | Species), zi=~0, disp=~1
## Final8.1: KUD50 ~ LengthStd + Habitat + MonitArea_km2 + (1 | Transmitter) + , zi=~0, disp=~1
## Final8.1:     (1 | Species), zi=~0, disp=~1
##                                                                                                              Df
## glmmTMB(KUD50 ~ Habitat + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log"))  6
## Final8.1                                                                                                      8
##                                                                                                                 AIC
## glmmTMB(KUD50 ~ Habitat + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log")) -76178
## Final8.1                                                                                                     -76254
##                                                                                                                 BIC
## glmmTMB(KUD50 ~ Habitat + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log")) -76130
## Final8.1                                                                                                     -76188
##                                                                                                              logLik
## glmmTMB(KUD50 ~ Habitat + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log"))  38095
## Final8.1                                                                                                      38135
##                                                                                                              deviance
## glmmTMB(KUD50 ~ Habitat + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log"))   -76190
## Final8.1                                                                                                       -76270
##                                                                                                               Chisq
## glmmTMB(KUD50 ~ Habitat + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log"))       
## Final8.1                                                                                                     79.131
##                                                                                                              Chi Df
## glmmTMB(KUD50 ~ Habitat + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log"))       
## Final8.1                                                                                                          2
##                                                                                                              Pr(>Chisq)
## glmmTMB(KUD50 ~ Habitat + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log"))           
## Final8.1                                                                                                      < 2.2e-16
##                                                                                                                 
## glmmTMB(KUD50 ~ Habitat + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log"))    
## Final8.1                                                                                                     ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(Final8.1, glmmTMB(KUD50 ~ Migration + (1|Transmitter) + (1|Species), data=week_kuds, family=Gamma(link="log")))
## Data: week_kuds
## Models:
## glmmTMB(KUD50 ~ Migration + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log")): KUD50 ~ Migration + (1 | Transmitter) + (1 | Species), zi=~0, disp=~1
## Final8.1: KUD50 ~ LengthStd + Habitat + MonitArea_km2 + (1 | Transmitter) + , zi=~0, disp=~1
## Final8.1:     (1 | Species), zi=~0, disp=~1
##                                                                                                                Df
## glmmTMB(KUD50 ~ Migration + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log"))  5
## Final8.1                                                                                                        8
##                                                                                                                   AIC
## glmmTMB(KUD50 ~ Migration + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log")) -76170
## Final8.1                                                                                                       -76254
##                                                                                                                   BIC
## glmmTMB(KUD50 ~ Migration + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log")) -76129
## Final8.1                                                                                                       -76188
##                                                                                                                logLik
## glmmTMB(KUD50 ~ Migration + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log"))  38090
## Final8.1                                                                                                        38135
##                                                                                                                deviance
## glmmTMB(KUD50 ~ Migration + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log"))   -76180
## Final8.1                                                                                                         -76270
##                                                                                                                 Chisq
## glmmTMB(KUD50 ~ Migration + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log"))       
## Final8.1                                                                                                       89.968
##                                                                                                                Chi Df
## glmmTMB(KUD50 ~ Migration + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log"))       
## Final8.1                                                                                                            3
##                                                                                                                Pr(>Chisq)
## glmmTMB(KUD50 ~ Migration + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log"))           
## Final8.1                                                                                                        < 2.2e-16
##                                                                                                                   
## glmmTMB(KUD50 ~ Migration + (1 | Transmitter) + (1 | Species), data = week_kuds, family = Gamma(link = "log"))    
## Final8.1                                                                                                       ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

We reached the best fitted model. Now we must analyse the residuals to see how they behave.

#Test residuals for the best model (goodness of fit)
testDispersion(Final8.1)  #plot with normality, dispersion and outliers
## 
##  DHARMa nonparametric dispersion test via sd of residuals fitted vs.
##  simulated
## 
## data:  simulationOutput
## dispersion = 1.4679, p-value = 0.008
## alternative hypothesis: two.sided
simulationOutput1 <- simulateResiduals(fittedModel = Final8.1, plot = F) #dispersion test
testDispersion(simulationOutput1) #dispersion test

## 
##  DHARMa nonparametric dispersion test via sd of residuals fitted vs.
##  simulated
## 
## data:  simulationOutput
## dispersion = 1.4679, p-value = 0.008
## alternative hypothesis: two.sided
plot(simulationOutput1) #residual analysis

plotQQunif(simulationOutput1) #Q-Q plot (normality checking)

plotResiduals(simulationOutput1) #residual vs. predicted (homoscedasticity checking)

testOutliers(simulationOutput1) #outliers checking

## 
##  DHARMa outlier test based on exact binomial test with approximate
##  expectations
## 
## data:  simulationOutput1
## outliers at both margin(s) = 254, observations = 25612, p-value =
## 0.0006481
## alternative hypothesis: true probability of success is not equal to 0.007968127
## 95 percent confidence interval:
##  0.008740014 0.011207540
## sample estimates:
## frequency of outliers (expected: 0.00796812749003984 ) 
##                                            0.009917226
#Simulations from the model
getObservedResponse(Final8.1)  #response used to fit the model
##     [1] 0.216 0.180 0.575 0.176 0.185 0.165 0.171 0.207 0.192 0.361 0.227 0.340
##    [13] 0.469 0.295 0.165 0.165 0.189 0.222 0.245 0.251 0.256 0.219 0.241 0.187
##    [25] 0.255 0.190 0.189 0.188 0.190 0.181 0.473 0.490 0.510 0.360 0.528 0.463
##    [37] 0.469 0.521 0.444 0.467 0.477 0.494 0.308 0.393 0.463 0.480 0.469 0.490
##    [49] 0.456 0.417 0.345 0.209 0.274 0.423 0.422 0.470 0.388 0.478 0.426 0.270
##    [61] 0.309 0.262 0.441 0.466 0.273 0.334 0.458 0.614 0.454 0.464 0.433 0.503
##    [73] 0.467 0.428 0.385 0.326 0.347 0.395 0.349 0.395 0.474 0.405 0.436 0.458
##    [85] 0.188 0.406 0.240 0.216 0.192 0.210 0.199 0.183 0.199 0.198 0.200 0.202
##    [97] 0.232 0.205 0.182 0.184 0.182 0.173 0.180 0.185 0.180 0.185 0.177 0.178
##   [109] 0.176 0.219 0.277 0.243 0.232 0.248 0.225 0.257 0.209 0.210 0.294 0.204
##   [121] 0.191 0.189 0.246 0.243 0.195 0.195 0.186 0.280 0.209 0.222 0.190 0.188
##   [133] 0.207 0.290 0.374 0.210 0.364 0.300 0.206 0.180 0.203 0.211 0.213 0.183
##   [145] 0.237 0.207 0.231 0.257 0.235 0.210 0.203 0.252 0.232 0.224 0.246 0.257
##   [157] 0.194 0.277 0.250 0.249 0.281 0.266 0.189 0.287 0.289 0.279 0.273 0.288
##   [169] 0.276 0.266 0.251 0.193 0.180 0.198 0.272 0.230 0.184 0.169 0.187 0.293
##   [181] 0.311 0.317 0.270 0.188 0.243 0.375 0.245 0.216 0.340 0.454 0.194 0.264
##   [193] 0.197 0.205 0.284 0.215 0.273 0.177 0.196 0.176 0.203 0.229 0.222 0.201
##   [205] 0.196 0.210 0.202 0.271 0.193 0.220 0.226 0.231 0.234 0.235 0.232 0.200
##   [217] 0.252 0.408 0.556 0.340 0.211 0.192 0.497 0.365 0.196 0.194 0.216 0.473
##   [229] 0.337 0.380 0.468 0.358 0.283 0.426 0.269 0.200 0.285 0.229 0.302 0.209
##   [241] 0.214 0.199 0.181 0.193 0.183 0.171 0.172 0.175 0.201 0.170 0.169 0.170
##   [253] 0.193 0.195 0.223 0.211 0.254 0.260 0.339 0.286 0.259 0.250 0.269 0.252
##   [265] 0.292 0.325 0.245 0.237 0.210 0.389 0.277 0.218 0.219 0.256 0.376 0.367
##   [277] 0.330 0.401 0.279 0.230 0.278 0.258 0.196 0.246 0.290 0.240 0.205 0.188
##   [289] 0.192 0.189 0.177 0.245 0.199 0.225 0.218 0.245 0.270 0.247 0.225 0.273
##   [301] 0.272 0.263 0.255 0.281 0.258 0.251 0.261 0.243 0.245 0.220 0.221 0.232
##   [313] 0.234 0.226 0.198 0.211 0.227 0.257 0.259 0.260 0.275 0.234 0.265 0.172
##   [325] 0.189 0.186 0.248 0.234 0.269 0.233 0.210 0.235 0.348 0.249 0.279 0.249
##   [337] 0.233 0.236 0.208 0.290 0.232 0.209 0.291 0.252 0.337 0.271 0.343 0.301
##   [349] 0.344 0.273 0.321 0.329 0.483 0.455 0.365 0.467 0.574 0.241 0.314 0.591
##   [361] 0.588 0.374 0.279 0.230 0.574 0.435 0.545 0.314 0.737 0.341 0.414 0.500
##   [373] 0.234 0.349 0.249 0.248 0.220 0.350 0.184 0.177 0.206 0.212 0.279 0.196
##   [385] 0.307 0.172 0.169 0.166 0.168 0.165 0.168 0.167 0.167 0.167 0.168 0.266
##   [397] 0.220 0.186 0.251 0.276 0.191 0.183 0.196 0.219 0.199 0.174 0.192 0.233
##   [409] 0.209 0.200 0.261 0.239 0.311 0.217 0.271 0.271 0.303 0.281 0.247 0.225
##   [421] 0.299 0.291 0.234 0.221 0.232 0.242 0.201 0.265 0.191 0.190 0.187 0.181
##   [433] 0.185 0.178 0.175 0.173 0.176 0.200 0.189 0.177 0.176 0.177 0.172 0.171
##   [445] 0.173 0.172 0.179 0.186 0.199 0.195 0.175 0.189 0.212 0.212 0.314 0.249
##   [457] 0.218 0.217 0.210 0.350 0.172 0.177 0.192 0.190 0.187 0.195 0.201 0.190
##   [469] 0.178 0.191 0.201 0.197 0.187 0.178 0.222 0.185 0.187 0.176 0.201 0.182
##   [481] 0.211 0.190 0.225 0.253 0.217 0.218 0.272 0.216 0.211 0.198 0.191 0.217
##   [493] 0.187 0.167 0.173 0.248 0.224 0.185 0.167 0.170 0.292 0.287 0.276 0.219
##   [505] 0.188 0.227 0.408 0.319 0.200 0.400 0.276 0.238 0.287 0.194 0.234 0.213
##   [517] 0.215 0.170 0.175 0.282 0.316 0.269 0.293 0.269 0.240 0.225 0.212 0.222
##   [529] 0.264 0.293 0.267 0.222 0.298 0.302 0.323 0.315 0.277 0.282 0.332 0.312
##   [541] 0.179 0.242 0.299 0.334 0.435 0.292 0.321 0.338 0.383 0.241 0.332 0.307
##   [553] 0.316 0.240 0.300 0.309 0.289 0.252 0.319 0.295 0.307 0.276 0.326 0.288
##   [565] 0.271 0.275 0.289 0.285 0.305 0.250 0.263 0.371 0.178 0.174 0.172 0.170
##   [577] 0.171 0.171 0.176 0.174 0.169 0.169 0.171 0.172 0.173 0.175 0.170 0.189
##   [589] 0.196 0.183 0.179 0.180 0.174 0.172 0.181 0.171 0.171 0.170 0.173 0.176
##   [601] 0.168 0.167 0.172 0.180 0.167 0.167 0.168 0.168 0.168 0.168 0.174 0.166
##   [613] 0.172 0.167 0.169 0.169 0.171 0.187 0.170 0.173 0.173 0.169 0.175 0.174
##   [625] 0.170 0.261 0.252 0.270 0.271 0.260 0.277 0.261 0.269 0.279 0.283 0.275
##   [637] 0.274 0.265 0.282 0.271 0.276 0.275 0.283 0.273 0.276 0.275 0.286 0.232
##   [649] 0.236 0.228 0.236 0.365 0.262 0.316 0.381 0.293 0.278 0.272 0.242 0.265
##   [661] 0.341 0.370 0.352 0.252 0.282 0.289 0.308 0.259 0.267 0.307 0.182 0.221
##   [673] 0.254 0.278 0.263 0.270 0.178 0.172 0.177 0.170 0.175 0.177 0.188 0.175
##   [685] 0.171 0.173 0.171 0.171 0.183 0.172 0.169 0.180 0.176 0.173 0.177 0.199
##   [697] 0.252 0.170 0.215 0.268 0.292 0.273 0.386 0.174 0.169 0.175 0.180 0.174
##   [709] 0.171 0.295 0.295 0.337 0.330 0.276 0.279 0.344 0.432 0.298 0.275 0.334
##   [721] 0.401 0.365 0.287 0.381 0.333 0.390 0.289 0.254 0.270 0.330 0.365 0.255
##   [733] 0.215 0.239 0.260 0.350 0.377 0.251 0.229 0.338 0.326 0.285 0.302 0.252
##   [745] 0.251 0.251 0.349 0.286 0.218 0.280 0.245 0.222 0.244 0.299 0.393 0.255
##   [757] 0.297 0.296 0.464 0.370 0.429 0.457 0.407 0.391 0.293 0.282 0.405 0.414
##   [769] 0.401 0.373 0.384 0.393 0.417 0.393 0.488 0.431 0.247 0.271 0.422 0.522
##   [781] 0.488 0.367 0.185 0.269 0.479 0.318 0.204 0.394 0.200 0.207 0.176 0.200
##   [793] 0.201 0.276 0.360 0.324 0.371 0.318 0.232 0.364 0.390 0.366 0.286 0.220
##   [805] 0.360 0.387 0.222 0.201 0.187 0.203 0.237 0.199 0.172 0.174 0.189 0.169
##   [817] 0.230 0.182 0.165 0.165 0.258 0.177 0.330 0.457 0.385 0.321 0.356 0.492
##   [829] 0.438 0.307 0.264 0.416 0.479 0.363 0.272 0.569 0.337 0.249 0.248 0.291
##   [841] 0.241 0.447 0.290 0.195 0.185 0.211 0.256 0.345 0.187 0.283 0.428 0.426
##   [853] 0.445 0.361 0.418 0.532 0.466 0.396 0.244 0.247 0.274 0.214 0.183 0.180
##   [865] 0.424 0.284 0.377 0.222 0.400 0.265 0.264 0.242 0.169 0.180 0.164 0.165
##   [877] 0.165 0.165 0.165 0.223 0.259 0.252 0.268 0.279 0.281 0.242 0.269 0.297
##   [889] 0.285 0.300 0.308 0.375 0.300 0.233 0.410 0.338 0.330 0.194 0.305 0.259
##   [901] 0.248 0.324 0.199 0.333 0.165 0.165 0.165 0.164 0.165 0.162 0.162 0.165
##   [913] 0.163 0.162 0.165 0.162 0.165 0.164 0.165 0.164 0.165 0.165 0.165 0.165
##   [925] 0.166 0.164 0.375 0.233 0.181 0.223 0.311 0.235 0.304 0.386 0.276 0.297
##   [937] 0.331 0.253 0.276 0.170 0.166 0.190 0.178 0.165 0.166 0.165 0.164 0.164
##   [949] 0.164 0.164 0.164 0.165 0.164 0.164 0.162 0.188 0.189 0.197 0.219 0.206
##   [961] 0.274 0.245 0.292 0.337 0.260 0.260 0.184 0.270 0.314 0.395 0.348 0.446
##   [973] 0.510 0.292 0.168 0.265 0.194 0.176 0.379 0.418 0.396 0.307 0.320 0.397
##   [985] 0.442 0.463 0.360 0.489 0.475 0.465 0.359 0.484 0.420 0.375 0.387 0.165
##   [997] 0.260 0.283 0.222 0.265 0.190 0.193 0.531 0.177 0.220 0.233 0.251 0.190
##  [1009] 0.271 0.175 0.331 0.316 0.337 0.253 0.260 0.245 0.214 0.235 0.190 0.183
##  [1021] 0.171 0.180 0.181 0.199 0.182 0.312 0.328 0.258 0.338 0.237 0.323 0.329
##  [1033] 0.328 0.164 0.597 0.199 0.203 0.215 0.328 0.227 0.298 0.303 0.325 0.278
##  [1045] 0.224 0.195 0.162 0.162 0.165 0.162 0.165 0.164 0.164 0.165 0.165 0.165
##  [1057] 0.164 0.165 0.530 0.385 0.162 0.164 0.164 0.165 0.165 0.163 0.164 0.163
##  [1069] 0.162 0.165 0.162 0.163 0.165 0.163 0.164 0.164 0.164 0.164 0.164 0.165
##  [1081] 0.165 0.164 0.165 0.164 0.165 0.164 0.165 0.165 0.165 0.165 0.164 0.162
##  [1093] 0.165 0.162 0.164 0.163 0.165 0.311 0.339 0.227 0.338 0.165 0.265 0.204
##  [1105] 0.209 0.192 0.232 0.172 0.174 0.172 0.193 0.194 0.207 0.222 0.248 0.212
##  [1117] 0.563 0.344 0.536 0.437 0.616 0.377 0.595 0.414 0.492 0.502 0.519 0.538
##  [1129] 0.579 0.359 0.382 0.369 0.440 0.402 0.474 0.574 1.394 1.494 1.843 0.560
##  [1141] 0.477 0.626 0.999 0.792 1.046 0.981 0.709 0.857 0.385 0.264 0.383 0.347
##  [1153] 0.252 0.256 0.236 0.182 0.195 0.191 0.176 0.171 0.208 0.232 0.224 0.303
##  [1165] 0.222 0.220 0.184 0.298 0.254 0.219 0.293 0.405 0.272 0.498 0.361 0.582
##  [1177] 0.344 0.555 0.272 0.500 0.221 0.402 0.277 0.574 0.278 0.568 0.334 0.449
##  [1189] 0.438 0.583 0.375 0.568 0.370 0.548 0.413 0.429 0.435 0.379 0.477 0.420
##  [1201] 0.294 0.346 0.463 0.473 0.426 0.424 0.248 0.437 0.219 0.336 0.207 0.359
##  [1213] 0.230 0.427 0.242 0.408 0.372 0.974 0.859 0.566 0.369 0.374 0.274 0.346
##  [1225] 0.285 0.527 0.617 0.271 0.247 0.270 0.252 0.211 0.184 0.223 0.202 0.192
##  [1237] 0.215 0.208 0.223 0.242 0.200 0.258 0.185 0.233 0.283 0.225 0.319 0.188
##  [1249] 0.224 0.207 0.208 0.271 0.332 0.326 0.395 0.450 0.514 0.364 0.390 0.306
##  [1261] 0.356 0.441 0.314 0.399 0.367 0.389 0.291 0.334 0.309 0.384 0.447 0.490
##  [1273] 0.463 0.368 0.529 0.258 0.349 0.433 0.258 0.395 0.236 0.275 0.232 0.232
##  [1285] 0.259 0.231 0.302 0.234 0.276 0.240 0.248 0.260 0.272 0.371 0.278 0.283
##  [1297] 0.245 0.247 0.311 0.244 0.294 0.287 0.428 0.453 0.449 0.478 0.686 0.611
##  [1309] 0.459 0.529 0.346 0.336 0.281 0.220 0.184 0.287 0.209 0.278 0.210 0.218
##  [1321] 0.233 0.297 0.280 0.193 0.190 0.217 0.246 0.350 0.290 0.273 0.305 0.194
##  [1333] 0.224 0.198 0.257 0.268 0.262 0.345 0.223 0.327 0.278 0.276 0.310 0.281
##  [1345] 0.239 0.285 0.226 0.352 0.245 0.294 0.253 0.334 0.329 0.268 0.461 0.292
##  [1357] 0.328 0.234 0.251 0.255 0.201 0.171 0.229 0.207 0.301 0.280 0.273 0.219
##  [1369] 0.252 0.199 0.329 0.251 0.325 0.301 0.408 0.260 0.181 0.180 0.195 0.168
##  [1381] 0.176 0.195 0.283 0.284 0.344 0.317 0.320 0.316 0.254 0.326 0.241 0.305
##  [1393] 0.179 0.164 0.164 0.165 0.165 0.164 0.180 0.165 0.165 0.168 0.193 0.164
##  [1405] 0.164 0.163 0.164 0.163 0.165 0.164 0.164 0.164 0.183 0.164 0.167 0.164
##  [1417] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.165 0.165 0.165 0.164
##  [1429] 0.164 0.170 0.165 0.165 0.168 0.208 0.164 0.164 0.162 0.163 0.164 0.163
##  [1441] 0.165 0.165 0.163 0.198 0.164 0.164 0.207 0.174 0.164 0.165 0.172 0.164
##  [1453] 0.164 0.164 0.164 0.164 0.164 0.164 0.294 0.330 0.237 0.163 0.164 0.163
##  [1465] 0.164 0.164 0.202 0.164 0.164 0.164 0.164 0.164 0.165 0.164 0.332 0.163
##  [1477] 0.164 0.195 0.163 0.164 0.164 0.190 0.164 0.164 0.164 0.164 0.164 0.164
##  [1489] 0.164 0.165 0.165 0.164 0.222 0.333 0.616 0.322 0.203 0.195 0.164 0.323
##  [1501] 0.164 0.323 0.322 0.458 0.387 0.165 0.548 0.682 0.402 0.254 0.624 0.223
##  [1513] 0.323 0.420 0.322 0.332 0.164 0.330 0.163 0.165 0.164 0.164 0.413 0.220
##  [1525] 0.713 0.408 0.608 0.307 0.701 0.682 0.164 0.464 0.249 0.164 0.164 0.164
##  [1537] 0.164 0.178 0.164 0.164 0.164 0.220 0.164 0.164 0.164 0.163 0.165 0.165
##  [1549] 0.164 0.163 0.164 0.165 0.162 0.165 0.164 0.165 0.165 0.165 0.164 0.163
##  [1561] 0.164 0.164 0.164 0.163 0.165 0.164 0.164 0.162 0.164 0.165 0.165 0.163
##  [1573] 0.165 0.164 0.164 0.165 0.319 0.186 0.262 0.165 0.164 0.164 0.164 0.164
##  [1585] 0.165 0.164 0.192 0.322 0.164 0.164 0.163 0.162 0.164 0.171 0.164 0.164
##  [1597] 0.164 0.165 0.165 0.331 0.170 0.246 0.165 0.164 0.164 0.164 0.164 0.165
##  [1609] 0.172 0.212 0.322 0.164 0.164 0.163 0.162 0.164 0.171 0.164 0.164 0.164
##  [1621] 0.164 0.236 0.236 0.465 0.302 0.218 0.277 0.174 0.199 0.163 0.164 0.165
##  [1633] 0.165 0.163 0.164 0.164 0.179 0.226 0.175 0.176 0.212 0.219 0.190 0.212
##  [1645] 0.229 0.459 0.268 0.219 0.249 0.195 0.182 0.171 0.178 0.163 0.165 0.165
##  [1657] 0.165 0.184 0.164 0.164 0.176 0.197 0.180 0.181 0.205 0.232 0.181 0.165
##  [1669] 0.164 0.165 0.164 0.331 0.343 0.322 0.182 0.162 0.163 0.164 0.178 0.162
##  [1681] 0.163 0.164 0.300 0.329 0.212 0.327 0.238 0.333 0.322 0.489 0.204 0.214
##  [1693] 0.256 0.165 0.165 0.295 0.371 0.162 0.617 0.418 0.588 0.211 0.162 0.187
##  [1705] 0.246 0.398 0.162 0.408 0.628 0.491 0.250 0.162 0.164 0.164 0.165 0.165
##  [1717] 0.162 0.165 0.164 0.165 0.198 0.745 0.240 0.418 0.407 0.627 0.432 0.258
##  [1729] 0.683 0.379 0.367 0.749 0.257 0.285 0.636 0.783 0.381 0.251 0.407 0.406
##  [1741] 0.211 0.565 0.679 0.580 0.365 0.394 0.472 0.362 0.355 0.165 0.165 0.162
##  [1753] 0.165 0.164 0.165 0.164 0.165 0.165 0.163 0.164 0.164 0.164 0.164 0.165
##  [1765] 0.165 0.162 0.164 0.165 0.164 0.165 0.165 0.164 0.164 0.222 0.162 0.164
##  [1777] 0.165 0.165 0.162 0.165 0.164 0.165 0.164 0.165 0.165 0.163 0.164 0.164
##  [1789] 0.164 0.164 0.163 0.164 0.204 0.163 0.191 0.165 0.164 0.165 0.165 0.165
##  [1801] 0.163 0.164 0.164 0.164 0.164 0.204 0.450 0.303 0.164 0.424 0.186 0.164
##  [1813] 0.222 0.528 0.406 0.187 0.427 0.216 0.164 0.255 0.243 0.190 0.173 0.301
##  [1825] 0.163 0.400 0.448 0.247 0.196 0.189 0.305 0.165 0.165 0.162 0.165 0.164
##  [1837] 0.165 0.164 0.165 0.165 0.163 0.164 0.164 0.164 0.164 0.164 0.165 0.162
##  [1849] 0.164 0.165 0.165 0.162 0.165 0.164 0.165 0.164 0.165 0.165 0.163 0.164
##  [1861] 0.164 0.164 0.164 0.164 0.173 0.184 0.190 0.162 0.163 0.164 0.176 0.192
##  [1873] 0.176 0.163 0.164 0.236 0.207 0.205 0.388 0.466 0.315 0.360 0.202 0.187
##  [1885] 0.180 0.368 0.466 0.329 0.339 0.238 0.277 0.294 0.223 0.827 0.197 0.248
##  [1897] 0.318 0.184 0.250 0.322 0.164 0.164 0.171 0.188 0.164 0.164 0.168 0.199
##  [1909] 0.400 0.319 0.400 0.380 0.164 0.164 0.164 0.164 0.164 0.164 0.349 0.188
##  [1921] 0.164 0.164 0.281 0.331 0.216 0.304 0.332 0.337 0.272 0.283 0.165 0.322
##  [1933] 0.238 0.346 0.400 0.254 0.498 0.235 0.165 0.322 0.211 0.374 0.352 0.376
##  [1945] 0.486 0.221 0.286 0.343 0.164 0.213 0.183 0.164 0.164 0.237 0.234 0.204
##  [1957] 0.164 0.164 0.319 0.277 0.274 0.178 0.164 0.164 0.243 0.272 0.293 0.171
##  [1969] 0.164 0.322 0.164 0.232 0.295 0.164 0.203 0.277 0.164 0.164 0.252 0.221
##  [1981] 0.162 0.163 0.164 0.164 0.227 0.249 0.172 0.200 0.297 0.164 0.164 0.249
##  [1993] 0.225 0.162 0.163 0.448 0.306 0.419 0.429 0.329 0.260 0.316 0.328 0.371
##  [2005] 0.299 0.409 0.416 0.339 0.274 0.345 0.297 0.164 0.164 0.164 0.322 0.165
##  [2017] 0.185 0.164 0.164 0.164 0.164 0.164 0.163 0.330 0.163 0.164 0.164 0.164
##  [2029] 0.324 0.165 0.181 0.164 0.164 0.164 0.164 0.164 0.163 0.329 0.163 0.300
##  [2041] 0.164 0.164 0.164 0.163 0.164 0.164 0.164 0.164 0.164 0.164 0.162 0.163
##  [2053] 0.307 0.164 0.164 0.164 0.163 0.164 0.164 0.164 0.164 0.164 0.164 0.162
##  [2065] 0.163 0.226 0.412 0.279 0.319 0.225 0.164 0.230 0.404 0.273 0.278 0.226
##  [2077] 0.164 0.164 0.164 0.312 0.168 0.164 0.164 0.164 0.164 0.164 0.164 0.164
##  [2089] 0.164 0.309 0.165 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.165
##  [2101] 0.164 0.164 0.164 0.165 0.164 0.318 0.204 0.193 0.191 0.189 0.171 0.327
##  [2113] 0.218 0.194 0.197 0.183 0.166 0.265 0.254 0.210 0.273 0.301 0.269 0.329
##  [2125] 0.313 0.330 0.251 0.215 0.183 0.267 0.204 0.331 0.237 0.235 0.253 0.215
##  [2137] 0.273 0.273 0.252 0.326 0.299 0.331 0.254 0.226 0.188 0.254 0.212 0.330
##  [2149] 0.293 0.192 0.489 0.202 0.165 0.164 0.164 0.632 0.167 0.182 0.166 0.177
##  [2161] 0.169 0.259 0.284 0.173 0.174 0.194 0.172 0.263 0.195 0.171 0.428 0.205
##  [2173] 0.185 0.165 0.268 0.356 0.224 0.187 0.173 0.194 0.250 0.173 0.231 0.197
##  [2185] 0.528 0.177 0.165 0.164 0.748 0.172 0.164 0.178 0.190 0.179 0.343 0.291
##  [2197] 0.175 0.173 0.199 0.179 0.236 0.190 0.168 0.456 0.189 0.184 0.165 0.243
##  [2209] 0.348 0.198 0.180 0.166 0.198 0.230 0.192 0.333 0.226 0.328 0.332 0.200
##  [2221] 0.333 0.211 0.252 0.320 0.164 0.218 0.288 0.239 0.330 0.326 0.269 0.333
##  [2233] 0.219 0.330 0.330 0.206 0.332 0.217 0.253 0.323 0.164 0.220 0.261 0.252
##  [2245] 0.329 0.331 0.269 0.162 1.049 0.171 0.165 0.269 0.235 0.168 0.202 0.620
##  [2257] 0.444 0.164 0.226 0.164 0.169 0.669 0.582 0.620 0.602 0.192 0.767 0.272
##  [2269] 0.172 0.833 0.734 0.523 0.623 0.319 0.165 0.382 0.503 0.331 0.168 0.164
##  [2281] 0.165 0.164 0.265 0.362 0.349 0.294 0.185 0.227 0.403 0.221 0.441 0.331
##  [2293] 0.445 0.456 0.288 0.187 0.251 0.492 0.244 0.347 0.297 0.479 0.272 0.308
##  [2305] 0.306 0.485 0.642 0.304 0.164 0.575 0.489 0.164 0.199 0.164 1.126 0.394
##  [2317] 0.736 0.686 0.337 0.343 0.471 0.337 0.184 0.724 0.251 0.534 0.611 0.379
##  [2329] 1.186 0.295 0.259 0.322 0.439 0.484 0.460 0.343 0.248 0.268 0.164 0.173
##  [2341] 0.742 0.196 0.257 0.347 0.817 0.164 0.165 0.920 1.098 0.698 0.229 0.442
##  [2353] 0.165 0.467 0.165 0.254 0.164 0.319 0.361 0.835 0.357 0.779 0.389 0.721
##  [2365] 0.781 0.776 0.443 0.870 0.799 0.736 0.424 0.165 0.165 0.285 0.267 0.170
##  [2377] 0.276 0.343 0.165 0.247 0.182 0.179 0.194 0.164 0.165 0.165 0.283 0.187
##  [2389] 0.165 0.165 0.165 0.165 0.164 0.165 0.165 0.164 0.171 0.165 0.164 0.209
##  [2401] 0.162 0.308 0.168 0.164 0.164 0.163 0.325 0.164 0.165 0.164 0.165 0.164
##  [2413] 0.165 0.165 0.165 0.165 0.164 0.165 0.164 0.165 0.164 0.165 0.165 0.164
##  [2425] 0.165 0.165 0.165 0.165 0.664 0.224 0.671 0.165 0.164 0.427 0.404 0.275
##  [2437] 0.361 0.310 0.441 0.507 0.740 0.694 0.666 0.627 0.528 0.432 0.362 0.425
##  [2449] 0.345 0.403 0.407 0.464 0.472 0.410 0.381 0.519 0.314 0.199 0.163 0.164
##  [2461] 0.453 0.220 0.239 0.236 0.498 0.287 0.266 0.273 0.245 0.216 0.684 0.163
##  [2473] 0.164 0.528 0.283 1.271 0.204 1.429 0.164 0.204 0.201 0.239 0.620 0.389
##  [2485] 0.366 0.321 0.310 0.164 0.959 0.718 0.211 0.316 0.427 0.685 0.163 0.550
##  [2497] 1.510 0.457 0.665 0.229 0.374 0.431 0.392 0.402 0.338 0.374 0.419 0.589
##  [2509] 0.455 0.360 0.509 0.235 0.388 0.869 0.268 0.684 0.545 0.400 0.345 0.388
##  [2521] 0.469 0.287 0.165 0.232 0.741 0.840 0.668 1.089 1.092 0.994 0.163 0.164
##  [2533] 0.211 0.164 0.543 0.330 0.524 0.903 0.370 0.536 0.948 0.274 0.349 0.406
##  [2545] 0.245 0.372 0.405 0.205 0.311 0.201 0.379 0.296 0.439 0.170 0.370 0.281
##  [2557] 0.375 0.193 0.419 0.257 0.212 0.298 0.283 0.415 0.293 0.429 0.374 0.285
##  [2569] 0.369 0.331 0.266 0.448 0.342 0.354 0.191 0.339 0.165 0.236 0.171 0.360
##  [2581] 0.165 0.215 0.165 0.218 0.165 0.330 0.408 0.165 0.344 0.165 0.430 0.458
##  [2593] 0.221 0.406 0.406 0.351 0.369 0.303 0.431 0.338 0.377 0.340 0.340 0.284
##  [2605] 0.402 0.381 0.530 0.350 0.449 0.486 0.478 0.362 0.321 0.233 0.312 0.398
##  [2617] 0.165 0.163 0.162 0.164 0.164 0.164 0.163 0.164 0.163 0.164 0.164 0.164
##  [2629] 0.164 0.164 0.165 0.163 0.217 0.327 0.185 0.163 0.162 0.164 0.164 0.164
##  [2641] 0.165 0.297 1.148 0.564 0.458 0.503 0.540 0.614 0.686 0.327 0.474 0.491
##  [2653] 0.259 0.248 0.225 0.397 0.231 0.209 0.199 0.245 0.443 0.327 0.165 0.164
##  [2665] 0.164 0.162 0.164 0.164 0.164 0.164 0.165 0.164 0.162 0.165 0.164 0.165
##  [2677] 0.164 0.165 0.163 0.165 0.169 0.206 0.174 0.167 0.165 0.164 0.166 0.165
##  [2689] 0.199 0.164 0.165 0.164 0.177 0.164 0.163 0.168 0.164 0.164 0.199 0.203
##  [2701] 0.183 0.215 0.176 0.165 0.162 0.163 0.164 0.164 0.164 0.164 0.165 0.163
##  [2713] 0.164 0.164 0.804 1.049 0.423 1.496 0.959 1.009 0.511 1.330 1.166 1.510
##  [2725] 2.561 0.809 1.382 0.565 0.711 0.645 3.104 0.564 2.383 0.487 1.296 0.465
##  [2737] 0.506 1.168 1.557 1.180 0.613 0.941 0.815 0.274 0.525 0.377 0.423 0.376
##  [2749] 0.381 0.370 0.382 0.388 0.364 0.378 0.379 0.455 0.526 0.419 0.256 0.292
##  [2761] 0.295 0.315 0.244 0.337 0.361 0.447 0.378 0.338 0.423 0.384 0.337 0.350
##  [2773] 0.452 0.416 0.353 0.646 0.449 0.354 0.617 0.377 0.426 0.304 0.353 0.513
##  [2785] 0.313 0.632 0.494 0.466 0.903 0.522 0.602 0.702 0.442 0.437 0.667 0.693
##  [2797] 0.666 0.564 0.470 0.717 0.691 0.914 0.767 0.741 0.582 0.526 0.819 0.270
##  [2809] 0.712 1.441 0.480 1.140 0.758 0.528 0.366 0.519 0.690 0.458 0.882 0.663
##  [2821] 0.715 0.806 0.615 0.509 0.635 0.549 0.556 0.562 0.637 0.705 0.604 0.524
##  [2833] 0.831 0.352 0.430 0.239 0.402 0.276 0.288 0.250 0.273 0.261 0.377 0.415
##  [2845] 0.348 0.557 0.407 0.710 0.465 0.625 0.398 0.715 0.423 0.505 0.615 0.911
##  [2857] 0.326 0.739 0.734 0.778 0.668 0.546 0.641 0.618 0.479 0.709 0.565 0.497
##  [2869] 0.734 0.542 1.003 0.486 0.838 0.177 0.292 0.615 0.182 0.332 0.320 0.311
##  [2881] 0.519 0.389 0.333 0.379 0.376 0.345 0.345 0.404 0.406 0.374 0.342 0.304
##  [2893] 0.374 0.267 0.379 0.382 1.377 0.832 0.988 0.310 0.353 0.390 0.399 0.374
##  [2905] 0.328 0.361 0.337 0.301 0.312 0.334 0.397 0.164 0.165 0.165 0.165 0.165
##  [2917] 0.165 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.232 0.255 0.172
##  [2929] 0.268 0.316 0.320 0.232 0.333 0.165 0.259 0.165 0.322 0.296 0.182 0.165
##  [2941] 0.248 0.233 0.238 0.479 0.371 0.278 0.329 0.394 0.217 0.217 0.234 0.259
##  [2953] 0.247 0.294 0.244 0.278 0.365 0.218 0.274 0.291 0.206 0.314 0.278 0.369
##  [2965] 0.351 0.328 0.372 0.353 0.230 0.216 0.165 0.325 0.341 0.308 0.282 0.194
##  [2977] 0.325 0.328 0.290 0.279 0.226 0.394 0.197 0.164 0.285 0.451 0.264 0.251
##  [2989] 0.164 0.164 0.164 0.310 0.378 0.503 0.388 0.512 0.177 0.185 0.178 0.164
##  [3001] 0.164 0.164 0.167 0.166 0.166 0.167 0.164 0.164 0.164 0.165 0.173 0.238
##  [3013] 0.239 0.223 0.220 0.229 0.177 0.238 0.194 0.210 0.219 0.224 0.221 0.166
##  [3025] 0.213 0.167 0.168 0.173 0.166 0.165 0.166 0.169 0.171 0.166 0.167 0.166
##  [3037] 0.166 0.170 0.175 0.174 0.170 0.169 0.179 0.167 0.166 0.165 0.165 0.165
##  [3049] 0.165 0.242 0.243 0.236 0.204 0.212 0.242 0.265 0.256 0.340 0.348 0.237
##  [3061] 0.357 0.284 0.416 0.163 0.165 0.258 0.232 0.248 0.247 0.245 0.258 0.283
##  [3073] 0.259 0.265 0.203 0.302 0.302 0.274 0.248 0.384 0.243 0.221 0.464 0.464
##  [3085] 0.479 0.398 0.356 0.216 0.219 0.192 0.253 0.240 0.234 0.164 0.166 0.164
##  [3097] 0.222 0.176 0.166 0.173 0.177 0.194 0.210 0.192 0.196 0.204 0.230 0.221
##  [3109] 0.214 0.173 0.175 0.260 0.200 0.180 0.166 0.164 0.164 0.165 0.164 0.165
##  [3121] 0.164 0.164 0.164 0.164 0.164 0.165 0.163 0.164 0.163 0.163 0.163 0.168
##  [3133] 0.174 0.164 0.164 0.164 0.165 0.167 0.172 0.167 0.182 0.167 0.172 0.211
##  [3145] 0.225 0.218 0.221 0.227 0.239 0.305 0.335 0.214 0.325 0.200 0.362 0.170
##  [3157] 0.162 0.181 0.164 0.169 0.164 0.163 0.164 0.164 0.163 0.164 0.163 0.166
##  [3169] 0.165 0.163 0.163 0.163 0.163 0.165 0.164 0.171 0.164 0.164 0.165 0.166
##  [3181] 0.172 0.164 0.186 0.186 0.164 0.164 0.167 0.166 0.166 0.165 0.182 0.176
##  [3193] 0.165 0.164 0.176 0.187 0.190 0.185 0.187 0.186 0.187 0.191 0.191 0.179
##  [3205] 0.185 0.185 0.184 0.180 0.181 0.190 0.180 0.193 0.269 0.242 0.311 0.168
##  [3217] 0.189 0.177 0.193 0.168 0.181 0.165 0.165 0.165 0.165 0.165 0.165 0.194
##  [3229] 0.173 0.165 0.165 0.164 0.205 0.165 0.215 0.164 0.268 0.262 0.220 0.164
##  [3241] 0.176 0.177 0.180 0.186 0.173 0.184 0.213 0.165 0.193 0.181 0.186 0.193
##  [3253] 0.208 0.200 0.165 0.165 0.183 0.181 0.170 0.164 0.191 0.237 0.203 0.509
##  [3265] 0.287 0.508 0.196 0.766 0.516 0.645 0.244 0.502 0.322 0.556 0.227 0.442
##  [3277] 0.298 0.514 0.381 0.500 0.336 0.603 0.316 0.325 0.164 0.233 0.383 0.398
##  [3289] 0.164 0.207 0.164 0.164 0.227 0.204 0.165 0.457 0.422 0.234 0.482 0.693
##  [3301] 0.580 0.374 0.325 0.453 0.356 0.186 0.396 0.445 0.164 0.245 0.344 0.256
##  [3313] 0.334 0.190 0.205 0.165 0.204 0.164 0.165 0.221 0.199 0.165 0.164 0.174
##  [3325] 0.164 0.165 0.165 0.165 0.165 0.306 0.238 0.207 0.290 0.212 0.196 0.178
##  [3337] 0.175 0.202 0.191 0.179 0.175 0.214 0.250 0.186 0.257 0.274 0.257 0.220
##  [3349] 0.207 0.499 0.357 0.217 0.225 0.230 0.261 0.232 0.221 0.248 0.221 0.189
##  [3361] 0.229 0.193 0.200 0.181 0.164 0.165 0.164 0.172 0.182 0.165 0.169 0.164
##  [3373] 0.164 0.296 0.205 0.192 0.204 0.268 0.203 0.184 0.183 0.192 0.188 0.197
##  [3385] 0.198 0.215 0.202 0.200 0.224 0.203 0.216 0.218 0.262 0.218 0.221 0.316
##  [3397] 0.212 0.262 0.274 0.250 0.279 0.254 0.235 0.232 0.227 0.173 0.183 0.168
##  [3409] 0.172 0.217 0.220 0.262 0.304 0.295 0.229 0.406 0.245 0.332 0.325 0.302
##  [3421] 0.224 0.246 0.277 0.250 0.230 0.187 0.232 0.238 0.235 0.202 0.200 0.179
##  [3433] 0.184 0.170 0.172 0.172 0.171 0.177 0.174 0.165 0.174 0.164 0.348 0.228
##  [3445] 0.164 0.213 0.245 0.189 0.254 0.168 0.209 0.170 0.250 0.244 0.235 0.194
##  [3457] 0.217 0.237 0.228 0.207 0.238 0.162 0.208 0.241 0.230 0.198 0.242 0.206
##  [3469] 0.171 0.177 0.171 0.173 0.219 0.213 0.188 0.174 0.175 0.164 0.178 0.164
##  [3481] 0.243 0.237 0.225 0.177 0.235 0.164 0.257 0.362 0.325 0.329 0.384 0.323
##  [3493] 0.321 0.270 0.289 0.351 0.323 0.245 0.336 0.332 0.321 0.277 0.420 0.300
##  [3505] 0.465 0.335 0.325 0.189 0.249 0.305 0.311 0.313 0.288 0.263 0.287 0.271
##  [3517] 0.274 0.244 0.299 0.330 0.191 0.164 0.164 0.164 0.164 0.164 0.164 0.185
##  [3529] 0.164 0.164 0.164 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165
##  [3541] 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.229
##  [3553] 0.165 0.165 0.165 0.259 0.165 0.165 0.165 0.165 0.347 0.165 0.165 0.165
##  [3565] 0.414 0.165 0.165 0.216 0.165 0.165 0.192 0.165 0.203 0.165 0.165 0.165
##  [3577] 0.165 0.354 0.165 0.165 0.165 0.334 0.165 0.165 0.165 0.165 0.165 0.210
##  [3589] 0.165 0.165 0.165 0.437 0.165 0.165 0.374 0.165 0.165 0.165 0.178 0.165
##  [3601] 0.165 0.165 0.165 0.165 0.165 0.177 0.165 0.165 0.165 0.165 0.165 0.165
##  [3613] 0.165 0.165 0.323 0.165 0.165 0.165 0.178 0.165 0.165 0.165 0.263 0.165
##  [3625] 0.165 0.165 0.165 0.195 0.165 0.165 0.175 0.165 0.165 0.165 0.178 0.165
##  [3637] 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165
##  [3649] 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165
##  [3661] 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165
##  [3673] 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165
##  [3685] 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.303 0.226
##  [3697] 0.182 0.165 0.165 0.165 0.214 0.165 0.189 0.165 0.165 0.165 0.249 0.165
##  [3709] 0.210 0.165 0.249 0.165 0.316 0.165 0.287 0.165 0.271 0.165 0.165 0.304
##  [3721] 0.165 0.165 0.231 0.165 0.165 0.165 0.222 0.165 0.165 0.203 0.165 0.165
##  [3733] 0.205 0.165 0.165 0.180 0.165 0.165 0.165 0.190 0.165 0.165 0.165 0.164
##  [3745] 0.165 0.165 0.165 0.163 0.165 0.165 0.165 0.163 0.165 0.165 0.165 0.163
##  [3757] 0.165 0.165 0.163 0.165 0.165 0.165 0.163 0.165 0.165 0.165 0.165 0.165
##  [3769] 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165
##  [3781] 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165
##  [3793] 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165
##  [3805] 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165
##  [3817] 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165
##  [3829] 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.199 0.246
##  [3841] 0.240 0.222 0.210 0.199 0.174 0.174 0.170 0.177 0.187 0.168 0.164 0.164
##  [3853] 0.167 0.178 0.197 0.207 0.186 0.175 0.219 0.228 0.227 0.165 0.172 0.167
##  [3865] 0.169 0.166 0.167 0.168 0.178 0.173 0.165 0.164 0.164 0.164 0.224 0.313
##  [3877] 0.293 0.245 0.191 0.246 0.296 0.268 0.265 0.294 0.243 0.263 0.316 0.220
##  [3889] 0.298 0.305 0.282 0.334 0.186 0.307 0.268 0.239 0.188 0.169 0.177 0.186
##  [3901] 0.175 0.191 0.193 0.167 0.164 0.164 0.168 0.164 0.164 0.170 0.164 0.164
##  [3913] 0.164 0.164 0.215 0.194 0.214 0.271 0.230 0.332 0.244 0.220 0.189 0.188
##  [3925] 0.168 0.181 0.216 0.207 0.197 0.184 0.233 0.285 0.205 0.332 0.182 0.238
##  [3937] 0.227 0.288 0.327 0.325 0.290 0.336 0.340 0.256 0.259 0.218 0.260 0.335
##  [3949] 0.343 0.287 0.353 0.339 0.317 0.273 0.253 0.231 0.192 0.170 0.336 0.334
##  [3961] 0.253 0.268 0.272 0.290 0.283 0.291 0.173 0.179 0.190 0.192 0.183 0.169
##  [3973] 0.173 0.166 0.164 0.164 0.164 0.164 0.172 0.164 0.164 0.247 0.204 0.230
##  [3985] 0.164 0.164 0.240 0.199 0.164 0.168 0.164 0.170 0.163 0.163 0.163 0.165
##  [3997] 0.168 0.176 0.163 0.163 0.163 0.170 0.172 0.165 0.165 0.192 0.202 0.166
##  [4009] 0.169 0.188 0.164 0.168 0.180 0.183 0.182 0.184 0.185 0.187 0.176 0.171
##  [4021] 0.176 0.174 0.195 0.184 0.179 0.187 0.206 0.220 0.191 0.180 0.166 0.178
##  [4033] 0.172 0.168 0.180 0.183 0.182 0.184 0.185 0.187 0.176 0.171 0.176 0.174
##  [4045] 0.195 0.181 0.169 0.215 0.187 0.192 0.164 0.164 0.164 0.164 0.165 0.164
##  [4057] 0.164 0.164 0.164 0.164 0.164 0.164 0.167 0.162 0.298 0.247 0.270 0.168
##  [4069] 0.163 0.167 0.167 0.193 0.241 0.166 0.172 0.172 0.210 0.178 0.177 0.188
##  [4081] 0.227 0.169 0.228 0.218 0.222 0.171 0.172 0.174 0.177 0.165 0.171 0.201
##  [4093] 0.170 0.179 0.207 0.172 0.170 0.169 0.180 0.181 0.168 0.168 0.176 0.172
##  [4105] 0.172 0.173 0.170 0.166 0.175 0.174 0.173 0.167 0.173 0.163 0.167 0.184
##  [4117] 0.269 0.170 0.170 0.164 0.195 0.164 0.229 0.214 0.198 0.186 0.199 0.204
##  [4129] 0.222 0.221 0.215 0.245 0.219 0.204 0.224 0.220 0.247 0.221 0.232 0.240
##  [4141] 0.232 0.220 0.218 0.200 0.232 0.248 0.261 0.231 0.240 0.227 0.225 0.241
##  [4153] 0.233 0.225 0.219 0.228 0.249 0.227 0.233 0.244 0.240 0.233 0.240 0.255
##  [4165] 0.238 0.236 0.249 0.263 0.244 0.237 0.237 0.245 0.222 0.257 0.211 0.240
##  [4177] 0.207 0.228 0.385 0.273 0.253 0.206 0.164 0.164 0.169 0.290 0.349 0.338
##  [4189] 0.399 0.300 0.337 0.332 0.227 0.244 0.279 0.240 0.214 0.239 0.255 0.241
##  [4201] 0.248 0.257 0.220 0.207 0.226 0.213 0.234 0.200 0.223 0.239 0.209 0.225
##  [4213] 0.233 0.226 0.221 0.217 0.318 0.308 0.290 0.280 0.245 0.188 0.201 0.225
##  [4225] 0.222 0.216 0.206 0.251 0.248 0.211 0.180 0.178 0.178 0.176 0.188 0.182
##  [4237] 0.172 0.167 0.172 0.181 0.178 0.184 0.172 0.178 0.194 0.185 0.176 0.174
##  [4249] 0.174 0.173 0.180 0.197 0.185 0.196 0.176 0.194 0.195 0.200 0.203 0.246
##  [4261] 0.221 0.227 0.222 0.215 0.213 0.230 0.278 0.246 0.251 0.217 0.221 0.241
##  [4273] 0.231 0.252 0.205 0.206 0.247 0.256 0.245 0.230 0.207 0.233 0.260 0.260
##  [4285] 0.238 0.251 0.230 0.252 0.269 0.275 0.272 0.261 0.236 0.246 0.231 0.256
##  [4297] 0.233 0.233 0.226 0.228 0.247 0.212 0.190 0.194 0.207 0.199 0.190 0.214
##  [4309] 0.213 0.237 0.242 0.240 0.260 0.246 0.276 0.249 0.263 0.254 0.282 0.271
##  [4321] 0.237 0.261 0.260 0.258 0.249 0.249 0.260 0.249 0.232 0.235 0.249 0.223
##  [4333] 0.234 0.222 0.196 0.256 0.260 0.208 0.253 0.276 0.187 0.182 0.182 0.171
##  [4345] 0.169 0.164 0.171 0.270 0.174 0.184 0.205 0.244 0.184 0.239 0.229 0.263
##  [4357] 0.246 0.222 0.284 0.270 0.263 0.268 0.209 0.265 0.230 0.244 0.276 0.228
##  [4369] 0.252 0.232 0.211 0.248 0.249 0.283 0.259 0.263 0.302 0.227 0.247 0.269
##  [4381] 0.236 0.266 0.211 0.254 0.272 0.252 0.283 0.196 0.254 0.259 0.256 0.257
##  [4393] 0.272 0.218 0.269 0.279 0.242 0.281 0.272 0.290 0.234 0.204 0.270 0.255
##  [4405] 0.257 0.269 0.241 0.227 0.262 0.250 0.261 0.262 0.232 0.231 0.252 0.258
##  [4417] 0.241 0.219 0.262 0.193 0.164 0.163 0.164 0.164 0.165 0.164 0.164 0.168
##  [4429] 0.172 0.166 0.169 0.164 0.164 0.164 0.164 0.163 0.165 0.165 0.209 0.240
##  [4441] 0.218 0.232 0.249 0.219 0.244 0.245 0.248 0.277 0.255 0.247 0.242 0.261
##  [4453] 0.249 0.235 0.237 0.240 0.230 0.253 0.238 0.216 0.235 0.228 0.211 0.243
##  [4465] 0.212 0.205 0.213 0.168 0.165 0.182 0.165 0.186 0.227 0.185 0.192 0.215
##  [4477] 0.222 0.183 0.181 0.183 0.223 0.204 0.197 0.197 0.241 0.170 0.211 0.196
##  [4489] 0.222 0.173 0.214 0.219 0.238 0.208 0.207 0.184 0.182 0.165 0.176 0.186
##  [4501] 0.183 0.164 0.164 0.165 0.185 0.182 0.201 0.215 0.187 0.171 0.180 0.185
##  [4513] 0.184 0.212 0.203 0.245 0.197 0.274 0.298 0.251 0.177 0.232 0.234 0.182
##  [4525] 0.171 0.190 0.208 0.188 0.182 0.200 0.179 0.195 0.199 0.226 0.282 0.242
##  [4537] 0.231 0.239 0.198 0.186 0.190 0.208 0.184 0.176 0.179 0.192 0.225 0.196
##  [4549] 0.186 0.175 0.174 0.183 0.193 0.183 0.217 0.166 0.182 0.204 0.254 0.319
##  [4561] 0.226 0.238 0.302 0.243 0.290 0.238 0.190 0.342 0.254 0.415 0.334 0.418
##  [4573] 0.341 0.252 0.344 0.433 0.404 0.380 0.302 0.198 0.281 0.320 0.315 0.296
##  [4585] 0.290 0.291 0.314 0.319 0.324 0.235 0.294 0.406 0.320 0.343 0.319 0.341
##  [4597] 0.317 0.228 0.240 0.314 0.390 0.378 0.342 0.337 0.344 0.318 0.281 0.294
##  [4609] 0.246 0.322 0.369 0.314 0.335 0.308 0.266 0.268 0.255 0.194 0.217 0.217
##  [4621] 0.225 0.178 0.317 0.223 0.252 0.224 0.253 0.264 0.242 0.174 0.178 0.209
##  [4633] 0.188 0.181 0.190 0.238 0.182 0.192 0.192 0.205 0.193 0.187 0.184 0.217
##  [4645] 0.232 0.210 0.191 0.188 0.198 0.208 0.232 0.190 0.182 0.207 0.185 0.247
##  [4657] 0.195 0.198 0.172 0.223 0.191 0.196 0.179 0.258 0.245 0.195 0.280 0.199
##  [4669] 0.187 0.243 0.236 0.203 0.223 0.249 0.232 0.222 0.237 0.215 0.236 0.245
##  [4681] 0.259 0.234 0.225 0.255 0.221 0.237 0.188 0.234 0.222 0.223 0.216 0.211
##  [4693] 0.207 0.209 0.196 0.205 0.182 0.195 0.214 0.214 0.181 0.219 0.219 0.220
##  [4705] 0.228 0.265 0.223 0.221 0.227 0.225 0.193 0.218 0.221 0.215 0.202 0.238
##  [4717] 0.240 0.208 0.276 0.269 0.248 0.294 0.300 0.252 0.259 0.260 0.256 0.270
##  [4729] 0.277 0.246 0.253 0.235 0.283 0.301 0.291 0.235 0.411 0.356 0.270 0.273
##  [4741] 0.242 0.325 0.319 0.330 0.333 0.311 0.338 0.339 0.374 0.314 0.358 0.272
##  [4753] 0.333 0.282 0.299 0.304 0.286 0.282 0.285 0.282 0.239 0.273 0.280 0.273
##  [4765] 0.317 0.270 0.289 0.235 0.342 0.326 0.240 0.308 0.266 0.182 0.330 0.255
##  [4777] 0.202 0.174 0.232 0.180 0.181 0.276 0.192 0.336 0.248 0.368 0.336 0.249
##  [4789] 0.170 0.207 0.216 0.318 0.165 0.282 0.212 0.268 0.311 0.233 0.215 0.205
##  [4801] 0.200 0.248 0.259 0.211 0.240 0.208 0.228 0.201 0.209 0.198 0.198 0.238
##  [4813] 0.265 0.373 0.333 0.269 0.255 0.327 0.188 0.244 0.204 0.355 0.303 0.266
##  [4825] 0.344 0.253 0.330 0.279 0.330 0.299 0.325 0.266 0.310 0.251 0.275 0.396
##  [4837] 0.402 0.375 0.372 0.340 0.307 0.341 0.378 0.274 0.398 0.262 0.382 0.370
##  [4849] 0.313 0.298 0.344 0.328 0.338 0.227 0.227 0.355 0.416 0.420 0.438 0.449
##  [4861] 0.415 0.395 0.297 0.396 0.217 0.289 0.251 0.228 0.219 0.228 0.229 0.259
##  [4873] 0.248 0.215 0.246 0.237 0.178 0.165 0.165 0.164 0.166 0.169 0.169 0.170
##  [4885] 0.168 0.168 0.173 0.179 0.185 0.183 0.172 0.170 0.181 0.185 0.165 0.169
##  [4897] 0.166 0.176 0.174 0.166 0.168 0.164 0.163 0.163 0.203 0.179 0.176 0.170
##  [4909] 0.186 0.177 0.178 0.172 0.169 0.181 0.170 0.177 0.165 0.186 0.193 0.240
##  [4921] 0.214 0.248 0.220 0.231 0.253 0.225 0.238 0.234 0.249 0.246 0.249 0.219
##  [4933] 0.225 0.252 0.236 0.228 0.234 0.219 0.258 0.252 0.187 0.234 0.214 0.206
##  [4945] 0.220 0.253 0.201 0.246 0.257 0.297 0.277 0.221 0.204 0.242 0.166 0.227
##  [4957] 0.240 0.241 0.244 0.235 0.252 0.254 0.230 0.251 0.188 0.249 0.254 0.223
##  [4969] 0.258 0.251 0.244 0.247 0.173 0.195 0.225 0.234 0.248 0.254 0.271 0.246
##  [4981] 0.256 0.277 0.285 0.246 0.177 0.206 0.258 0.257 0.265 0.256 0.242 0.250
##  [4993] 0.228 0.253 0.212 0.228 0.238 0.228 0.247 0.255 0.209 0.245 0.360 0.245
##  [5005] 0.174 0.238 0.237 0.300 0.174 0.169 0.168 0.166 0.413 0.169 0.164 0.173
##  [5017] 0.167 0.167 0.166 0.167 0.171 0.170 0.167 0.168 0.167 0.164 0.171 0.172
##  [5029] 0.238 0.164 0.163 0.163 0.164 0.176 0.176 0.203 0.164 0.164 0.164 0.164
##  [5041] 0.164 0.164 0.164 0.165 0.165 0.164 0.164 0.164 0.164 0.165 0.178 0.164
##  [5053] 0.164 0.164 0.164 0.164 0.166 0.164 0.164 0.164 0.164 0.164 0.164 0.177
##  [5065] 0.164 0.164 0.171 0.168 0.176 0.174 0.185 0.164 0.163 0.163 0.163 0.167
##  [5077] 0.165 0.178 0.164 0.164 0.165 0.164 0.165 0.164 0.165 0.166 0.176 0.167
##  [5089] 0.177 0.247 0.167 0.178 0.183 0.178 0.164 0.182 0.172 0.166 0.164 0.164
##  [5101] 0.164 0.164 0.164 0.164 0.164 0.181 0.170 0.171 0.170 0.179 0.174 0.178
##  [5113] 0.173 0.216 0.163 0.163 0.163 0.197 0.221 0.177 0.185 0.174 0.164 0.172
##  [5125] 0.164 0.164 0.166 0.166 0.167 0.170 0.168 0.164 0.180 0.177 0.187 0.164
##  [5137] 0.273 0.165 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.188 0.177
##  [5149] 0.172 0.177 0.172 0.183 0.178 0.250 0.241 0.163 0.250 0.289 0.210 0.350
##  [5161] 0.206 0.169 0.166 0.167 0.167 0.166 0.178 0.165 0.167 0.167 0.166 0.168
##  [5173] 0.170 0.164 0.167 0.165 0.164 0.186 0.169 0.165 0.164 0.165 0.173 0.171
##  [5185] 0.168 0.174 0.170 0.166 0.166 0.163 0.165 0.165 0.191 0.166 0.169 0.164
##  [5197] 0.164 0.188 0.165 0.173 0.167 0.172 0.168 0.165 0.166 0.168 0.168 0.166
##  [5209] 0.165 0.165 0.166 0.167 0.166 0.166 0.164 0.168 0.170 0.163 0.166 0.187
##  [5221] 0.181 0.164 0.163 0.163 0.163 0.173 0.165 0.165 0.165 0.164 0.167 0.164
##  [5233] 0.164 0.167 0.182 0.166 0.178 0.175 0.173 0.173 0.179 0.176 0.172 0.170
##  [5245] 0.174 0.170 0.170 0.175 0.176 0.176 0.176 0.177 0.178 0.179 0.176 0.176
##  [5257] 0.168 0.184 0.174 0.171 0.172 0.170 0.166 0.163 0.163 0.163 0.181 0.172
##  [5269] 0.167 0.175 0.169 0.167 0.169 0.169 0.165 0.178 0.168 0.166 0.166 0.170
##  [5281] 0.183 0.328 0.178 0.167 0.166 0.164 0.164 0.164 0.164 0.167 0.164 0.164
##  [5293] 0.164 0.164 0.206 0.164 0.182 0.193 0.205 0.180 0.164 0.164 0.179 0.164
##  [5305] 0.164 0.164 0.164 0.164 0.164 0.169 0.172 0.171 0.175 0.164 0.168 0.176
##  [5317] 0.264 0.164 0.163 0.184 0.227 0.182 0.169 0.164 0.165 0.171 0.167 0.165
##  [5329] 0.170 0.170 0.167 0.169 0.173 0.169 0.179 0.186 0.186 0.195 0.176 0.192
##  [5341] 0.189 0.210 0.179 0.169 0.174 0.173 0.175 0.180 0.178 0.182 0.170 0.180
##  [5353] 0.183 0.185 0.171 0.191 0.188 0.186 0.196 0.190 0.191 0.183 0.176 0.180
##  [5365] 0.193 0.201 0.298 0.350 0.178 0.169 0.165 0.166 0.165 0.165 0.166 0.166
##  [5377] 0.164 0.164 0.164 0.164 0.164 0.165 0.238 0.222 0.240 0.232 0.212 0.216
##  [5389] 0.233 0.225 0.190 0.165 0.167 0.169 0.164 0.164 0.170 0.179 0.178 0.164
##  [5401] 0.191 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.258 0.192 0.189 0.174
##  [5413] 0.250 0.186 0.223 0.165 0.241 0.247 0.165 0.165 0.164 0.165 0.165 0.165
##  [5425] 0.165 0.165 0.165 0.165 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
##  [5437] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
##  [5449] 0.298 0.243 0.163 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.162
##  [5461] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
##  [5473] 0.164 0.172 0.174 0.167 0.164 0.172 0.164 0.164 0.164 0.164 0.164 0.195
##  [5485] 0.246 0.280 0.167 0.168 0.165 0.165 0.181 0.165 0.235 0.367 0.187 0.180
##  [5497] 0.181 0.164 0.163 0.163 0.163 0.163 0.164 0.330 0.179 0.181 0.168 0.165
##  [5509] 0.164 0.166 0.164 0.164 0.164 0.164 0.164 0.164 0.169 0.166 0.266 0.164
##  [5521] 0.196 0.279 0.164 0.329 0.164 0.165 0.164 0.164 0.164 0.164 0.164 0.164
##  [5533] 0.202 0.213 0.191 0.192 0.220 0.177 0.217 0.169 0.163 0.173 0.188 0.164
##  [5545] 0.164 0.164 0.163 0.163 0.163 0.163 0.163 0.163 0.163 0.163 0.164 0.164
##  [5557] 0.361 0.162 0.162 0.165 0.165 0.435 0.216 0.204 0.204 0.331 0.163 0.165
##  [5569] 0.163 0.163 0.163 0.163 0.166 0.166 0.192 0.164 0.176 0.165 0.165 0.163
##  [5581] 0.163 0.164 0.164 0.164 0.165 0.165 0.264 0.233 0.211 0.249 0.238 0.438
##  [5593] 0.363 0.318 0.316 0.295 0.226 0.311 0.327 0.439 0.356 0.197 0.181 0.194
##  [5605] 0.164 0.164 0.165 0.165 0.164 0.302 0.229 0.273 0.262 0.368 0.163 0.163
##  [5617] 0.163 0.163 0.163 0.163 0.163 0.164 0.164 0.179 0.164 0.163 0.164 0.164
##  [5629] 0.165 0.164 0.164 0.179 0.190 0.221 0.265 0.270 0.295 0.256 0.267 0.289
##  [5641] 0.264 0.215 0.277 0.262 0.305 0.312 0.284 0.289 0.205 0.300 0.234 0.193
##  [5653] 0.189 0.197 0.659 0.322 0.232 0.221 0.167 0.318 0.253 0.509 0.214 0.163
##  [5665] 0.163 0.163 0.163 0.163 0.163 0.163 0.222 0.255 0.303 0.164 0.164 0.166
##  [5677] 0.167 0.170 0.164 0.166 0.165 0.169 0.173 0.167 0.170 0.167 0.168 0.175
##  [5689] 0.180 0.164 0.253 0.292 0.220 0.303 0.295 0.209 0.170 0.170 0.227 0.199
##  [5701] 0.259 0.311 0.257 0.179 0.183 0.164 0.169 0.170 0.192 0.255 0.368 0.347
##  [5713] 0.257 0.238 0.283 0.231 0.212 0.222 0.229 0.310 0.276 0.229 0.274 0.278
##  [5725] 0.311 0.325 0.262 0.272 0.229 0.277 0.205 0.314 0.307 0.311 0.288 0.343
##  [5737] 0.330 0.294 0.618 0.310 0.374 0.320 0.215 0.164 0.196 0.178 0.171 0.165
##  [5749] 0.212 0.195 0.243 0.264 0.270 0.167 0.176 0.230 0.168 0.184 0.165 0.167
##  [5761] 0.186 0.170 0.165 0.165 0.169 0.179 0.176 0.170 0.180 0.162 0.209 0.164
##  [5773] 0.210 0.164 0.164 0.164 0.194 0.194 0.231 0.236 0.225 0.307 0.201 0.179
##  [5785] 0.166 0.191 0.210 0.241 0.179 0.202 0.239 0.166 0.166 0.166 0.165 0.165
##  [5797] 0.166 0.172 0.176 0.165 0.165 0.165 0.164 0.162 0.164 0.165 0.165 0.163
##  [5809] 0.163 0.170 0.164 0.175 0.165 0.165 0.164 0.165 0.165 0.164 0.165 0.164
##  [5821] 0.165 0.165 0.165 0.165 0.165 0.165 0.164 0.165 0.165 0.165 0.164 0.165
##  [5833] 0.164 0.165 0.165 0.165 0.165 0.164 0.164 0.165 0.170 0.164 0.165 0.165
##  [5845] 0.163 0.255 0.309 0.366 0.165 0.165 0.164 0.164 0.165 0.165 0.165 0.165
##  [5857] 0.165 0.165 0.164 0.165 0.165 0.165 0.164 0.165 0.189 0.209 0.165 0.175
##  [5869] 0.164 0.164 0.165 0.173 0.164 0.336 0.282 0.275 0.244 0.308 0.575 0.164
##  [5881] 0.165 0.165 0.164 0.164 0.165 0.168 0.164 0.164 0.168 0.168 0.165 0.165
##  [5893] 0.190 0.165 0.164 0.196 0.165 0.176 0.164 0.165 0.277 0.208 0.165 0.303
##  [5905] 0.165 0.164 0.253 0.300 0.298 0.296 0.299 0.175 0.214 0.232 0.217 0.199
##  [5917] 0.179 0.213 0.207 0.177 0.199 0.237 0.169 0.170 0.169 0.189 0.240 0.169
##  [5929] 0.166 0.237 0.165 0.165 0.164 0.164 0.165 0.319 0.308 0.247 0.235 0.263
##  [5941] 0.236 0.240 0.229 0.309 0.323 0.292 0.215 0.357 0.165 0.165 0.347 0.371
##  [5953] 0.165 0.205 0.353 0.259 0.287 0.187 0.165 0.165 0.163 0.163 0.164 0.164
##  [5965] 0.165 0.165 0.248 0.369 0.355 0.164 0.165 0.165 0.165 0.164 0.165 0.165
##  [5977] 0.165 0.164 0.164 0.165 0.164 0.165 0.165 0.164 0.321 0.295 0.344 0.164
##  [5989] 0.163 0.165 0.164 0.164 0.164 0.163 0.163 0.163 0.163 0.163 0.163 0.163
##  [6001] 0.163 0.164 0.164 0.387 0.164 0.163 0.184 0.475 0.164 0.164 0.164 0.164
##  [6013] 0.163 0.165 0.164 0.164 0.164 0.163 0.163 0.163 0.163 0.163 0.163 0.163
##  [6025] 0.163 0.164 0.168 0.328 0.274 0.259 0.292 0.286 0.352 0.191 0.164 0.164
##  [6037] 0.164 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.203 0.202 0.181
##  [6049] 0.164 0.164 0.164 0.164 0.164 0.313 0.340 0.488 0.498 0.343 0.164 0.164
##  [6061] 0.164 0.163 0.163 0.163 0.163 0.163 0.163 0.163 0.163 0.176 0.164 0.234
##  [6073] 0.164 0.222 0.164 0.165 0.210 0.280 0.284 0.238 0.222 0.257 0.260 0.321
##  [6085] 0.292 0.195 0.229 0.176 0.195 0.234 0.331 0.219 0.349 0.249 0.168 0.173
##  [6097] 0.203 0.223 0.203 0.231 0.220 0.294 0.209 0.164 0.166 0.165 0.164 0.168
##  [6109] 0.165 0.164 0.164 0.164 0.165 0.164 0.164 0.164 0.165 0.165 0.165 0.165
##  [6121] 0.165 0.165 0.164 0.165 0.165 0.165 0.164 0.165 0.199 0.203 0.172 0.165
##  [6133] 0.165 0.163 0.164 0.164 0.164 0.165 0.165 0.196 0.192 0.284 0.200 0.172
##  [6145] 0.167 0.171 0.265 0.283 0.202 0.164 0.164 0.164 0.237 0.164 0.171 0.183
##  [6157] 0.222 0.219 0.188 0.165 0.184 0.164 0.164 0.165 0.165 0.164 0.180 0.194
##  [6169] 0.277 0.298 0.254 0.282 0.233 0.193 0.199 0.325 0.277 0.209 0.223 0.165
##  [6181] 0.227 0.183 0.267 0.195 0.331 0.187 0.164 0.171 0.169 0.205 0.183 0.210
##  [6193] 0.180 0.345 0.184 0.177 0.185 0.199 0.204 0.197 0.251 0.232 0.248 0.239
##  [6205] 0.266 0.276 0.238 0.220 0.283 0.258 0.282 0.281 0.304 0.291 0.243 0.266
##  [6217] 0.276 0.263 0.262 0.232 0.214 0.314 0.180 0.165 0.365 0.164 0.164 0.164
##  [6229] 0.163 0.163 0.163 0.163 0.163 0.163 0.163 0.163 0.164 0.164 0.196 0.164
##  [6241] 0.198 0.265 0.164 0.163 0.163 0.163 0.163 0.163 0.163 0.163 0.163 0.164
##  [6253] 0.181 0.163 0.164 0.163 0.204 0.207 0.337 0.219 0.248 0.267 0.244 0.304
##  [6265] 0.302 0.324 0.287 0.282 0.285 0.290 0.284 0.330 0.357 0.337 0.244 0.196
##  [6277] 0.361 0.216 0.257 0.271 0.298 0.323 0.254 0.264 0.191 0.165 0.227 0.175
##  [6289] 0.183 0.165 0.195 0.229 0.201 0.258 0.268 0.564 0.162 0.165 0.164 0.411
##  [6301] 0.223 0.228 0.169 0.167 0.169 0.165 0.193 0.168 0.165 0.175 0.179 0.167
##  [6313] 0.165 0.211 0.229 0.168 0.165 0.165 0.165 0.166 0.165 0.165 0.165 0.165
##  [6325] 0.173 0.164 0.199 0.162 0.165 0.164 0.164 0.164 0.236 0.197 0.267 0.183
##  [6337] 0.186 0.199 0.254 0.223 0.187 0.182 0.202 0.214 0.173 0.201 0.167 0.198
##  [6349] 0.162 0.227 0.164 0.236 0.189 0.168 0.183 0.211 0.228 0.164 0.164 0.164
##  [6361] 0.163 0.163 0.187 0.306 0.214 0.257 0.203 0.197 0.179 0.177 0.530 0.269
##  [6373] 0.303 0.257 0.249 0.258 0.170 0.167 0.243 0.189 0.237 0.320 0.243 0.245
##  [6385] 0.249 0.250 0.234 0.264 0.265 0.164 0.166 0.167 0.453 0.185 0.255 0.211
##  [6397] 0.177 0.168 0.186 0.176 0.180 0.234 0.212 0.251 0.267 0.237 0.210 0.214
##  [6409] 0.250 0.294 0.247 0.175 0.198 0.214 0.222 0.304 0.220 0.268 0.276 0.306
##  [6421] 0.230 0.272 0.220 0.223 0.302 0.320 0.286 0.261 0.164 0.255 0.164 0.163
##  [6433] 0.164 0.164 0.165 0.164 0.253 0.181 0.195 0.244 0.244 0.250 0.256 0.244
##  [6445] 0.279 0.294 0.314 0.289 0.279 0.246 0.279 0.297 0.206 0.206 0.360 0.163
##  [6457] 0.181 0.189 0.189 0.218 0.215 0.196 0.218 0.170 0.484 0.370 0.276 0.249
##  [6469] 0.310 0.309 0.229 0.275 0.214 0.247 0.300 0.164 0.312 0.305 0.316 0.212
##  [6481] 0.178 0.187 0.246 0.187 0.173 0.175 0.207 0.241 0.179 0.189 0.194 0.230
##  [6493] 0.689 0.214 0.165 0.368 0.213 0.419 0.274 0.176 0.184 0.193 0.164 0.165
##  [6505] 0.174 0.173 0.177 0.171 0.192 0.177 0.379 0.352 0.180 0.174 0.324 0.184
##  [6517] 0.164 0.165 0.168 0.164 0.164 0.165 0.165 0.162 0.164 0.170 0.163 0.164
##  [6529] 0.164 0.165 0.164 0.164 0.164 0.165 0.172 0.182 0.184 0.230 0.274 0.639
##  [6541] 0.292 0.182 0.192 0.624 0.164 0.166 0.162 0.180 0.199 0.165 0.164 0.162
##  [6553] 0.164 0.178 0.164 0.289 0.245 0.235 0.202 0.196 0.172 0.162 0.169 0.176
##  [6565] 0.170 0.174 0.200 0.162 0.253 0.262 0.171 0.232 0.262 0.227 0.220 0.221
##  [6577] 0.190 0.215 0.262 0.226 0.311 0.283 0.221 0.229 0.296 0.405 0.262 0.163
##  [6589] 0.163 0.163 0.163 0.163 0.164 0.164 0.163 0.164 0.163 0.163 0.163 0.163
##  [6601] 0.163 0.184 0.164 0.232 0.164 0.163 0.163 0.163 0.163 0.167 0.223 0.163
##  [6613] 0.263 0.166 0.164 0.268 0.302 0.232 0.304 0.238 0.165 0.180 0.352 0.288
##  [6625] 0.273 0.205 0.165 0.165 0.165 0.164 0.165 0.165 0.165 0.172 0.165 0.222
##  [6637] 0.169 0.180 0.165 0.167 0.164 0.164 0.165 0.165 0.164 0.165 0.165 0.165
##  [6649] 0.165 0.165 0.165 0.165 0.165 0.162 0.165 0.182 0.165 0.164 0.165 0.165
##  [6661] 0.165 0.165 0.165 0.165 0.165 0.162 0.337 0.165 0.167 0.165 0.204 0.340
##  [6673] 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.162 0.165 0.165 0.165
##  [6685] 0.165 0.162 0.168 0.166 0.164 0.169 0.193 0.176 0.185 0.168 0.211 0.194
##  [6697] 0.164 0.165 0.204 0.237 0.197 0.302 0.178 0.162 0.172 0.173 0.168 0.162
##  [6709] 0.171 0.162 0.172 0.164 0.164 0.162 0.162 0.174 0.164 0.164 0.164 0.164
##  [6721] 0.164 0.171 0.164 0.164 0.164 0.168 0.164 0.328 0.333 0.164 0.164 0.164
##  [6733] 0.263 0.165 0.292 0.166 0.165 0.164 0.162 0.163 0.163 0.164 0.164 0.162
##  [6745] 0.163 0.163 0.163 0.164 0.162 0.162 0.163 0.163 0.164 0.162 0.162 0.163
##  [6757] 0.163 0.163 0.162 0.162 0.163 0.165 0.164 0.162 0.162 0.163 0.163 0.164
##  [6769] 0.162 0.165 0.163 0.163 0.164 0.162 0.162 0.163 0.163 0.163 0.164 0.162
##  [6781] 0.162 0.163 0.163 0.163 0.162 0.165 0.169 0.163 0.163 0.164 0.162 0.166
##  [6793] 0.163 0.163 0.164 0.162 0.173 0.163 0.163 0.164 0.162 0.162 0.163 0.163
##  [6805] 0.164 0.162 0.162 0.163 0.165 0.169 0.162 0.162 0.163 0.185 0.164 0.162
##  [6817] 0.165 0.163 0.163 0.164 0.162 0.162 0.163 0.163 0.164 0.162 0.162 0.163
##  [6829] 0.165 0.163 0.162 0.162 0.163 0.163 0.164 0.162 0.163 0.163 0.164 0.162
##  [6841] 0.163 0.165 0.164 0.162 0.163 0.163 0.164 0.162 0.163 0.165 0.164 0.162
##  [6853] 0.163 0.163 0.164 0.162 0.163 0.163 0.164 0.162 0.163 0.163 0.164 0.162
##  [6865] 0.163 0.164 0.162 0.162 0.163 0.163 0.162 0.164 0.162 0.163 0.163 0.162
##  [6877] 0.163 0.162 0.162 0.163 0.162 0.162 0.163 0.162 0.162 0.163 0.164 0.162
##  [6889] 0.164 0.162 0.163 0.165 0.162 0.164 0.165 0.163 0.166 0.162 0.164 0.162
##  [6901] 0.163 0.165 0.162 0.164 0.162 0.163 0.164 0.162 0.164 0.162 0.163 0.163
##  [6913] 0.163 0.164 0.164 0.162 0.163 0.163 0.163 0.164 0.164 0.162 0.163 0.163
##  [6925] 0.163 0.164 0.164 0.162 0.163 0.163 0.164 0.164 0.164 0.162 0.163 0.163
##  [6937] 0.164 0.164 0.164 0.162 0.163 0.163 0.163 0.164 0.164 0.162 0.163 0.163
##  [6949] 0.163 0.164 0.164 0.164 0.163 0.163 0.163 0.164 0.164 0.162 0.163 0.163
##  [6961] 0.163 0.164 0.164 0.162 0.163 0.163 0.163 0.164 0.164 0.162 0.163 0.163
##  [6973] 0.163 0.164 0.164 0.162 0.163 0.163 0.165 0.164 0.164 0.162 0.163 0.163
##  [6985] 0.163 0.164 0.164 0.162 0.163 0.163 0.163 0.164 0.164 0.165 0.163 0.163
##  [6997] 0.164 0.165 0.164 0.162 0.163 0.163 0.165 0.164 0.162 0.163 0.163 0.165
##  [7009] 0.163 0.162 0.163 0.163 0.165 0.163 0.162 0.163 0.163 0.163 0.164 0.162
##  [7021] 0.163 0.165 0.165 0.162 0.162 0.163 0.163 0.165 0.164 0.165 0.163 0.163
##  [7033] 0.164 0.164 0.162 0.163 0.163 0.164 0.163 0.162 0.163 0.163 0.162 0.163
##  [7045] 0.163 0.163 0.163 0.164 0.162 0.163 0.163 0.163 0.165 0.163 0.163 0.163
##  [7057] 0.163 0.164 0.162 0.162 0.163 0.163 0.164 0.162 0.162 0.163 0.164 0.164
##  [7069] 0.162 0.162 0.163 0.165 0.164 0.163 0.165 0.163 0.163 0.164 0.162 0.162
##  [7081] 0.163 0.163 0.164 0.162 0.162 0.163 0.165 0.164 0.162 0.162 0.163 0.163
##  [7093] 0.164 0.162 0.163 0.163 0.164 0.162 0.163 0.165 0.164 0.162 0.163 0.163
##  [7105] 0.164 0.162 0.163 0.165 0.164 0.174 0.163 0.163 0.164 0.171 0.182 0.163
##  [7117] 0.163 0.172 0.209 0.247 0.213 0.169 0.168 0.179 0.314 0.166 0.172 0.262
##  [7129] 0.178 0.177 0.242 0.174 0.273 0.175 0.213 0.174 0.201 0.168 0.271 0.167
##  [7141] 0.305 0.169 0.209 0.167 0.163 0.194 0.165 0.163 0.162 0.222 0.162 0.163
##  [7153] 0.167 0.163 0.165 0.162 0.165 0.162 0.164 0.163 0.164 0.162 0.164 0.162
##  [7165] 0.165 0.162 0.164 0.162 0.164 0.162 0.164 0.162 0.163 0.163 0.164 0.164
##  [7177] 0.162 0.163 0.163 0.164 0.164 0.162 0.163 0.163 0.164 0.164 0.162 0.163
##  [7189] 0.164 0.164 0.164 0.162 0.163 0.164 0.164 0.164 0.162 0.163 0.163 0.164
##  [7201] 0.164 0.162 0.163 0.163 0.164 0.164 0.164 0.163 0.163 0.164 0.163 0.162
##  [7213] 0.163 0.163 0.164 0.164 0.162 0.163 0.163 0.165 0.164 0.162 0.163 0.163
##  [7225] 0.165 0.164 0.162 0.163 0.165 0.164 0.164 0.162 0.163 0.163 0.165 0.164
##  [7237] 0.162 0.163 0.163 0.164 0.172 0.165 0.163 0.165 0.165 0.164 0.162 0.163
##  [7249] 0.163 0.163 0.164 0.164 0.162 0.163 0.163 0.163 0.164 0.162 0.162 0.163
##  [7261] 0.163 0.163 0.164 0.162 0.162 0.163 0.163 0.163 0.163 0.162 0.162 0.163
##  [7273] 0.163 0.165 0.164 0.162 0.162 0.163 0.163 0.163 0.164 0.162 0.165 0.163
##  [7285] 0.163 0.163 0.164 0.162 0.162 0.163 0.163 0.163 0.164 0.162 0.162 0.163
##  [7297] 0.163 0.163 0.162 0.163 0.163 0.163 0.163 0.163 0.164 0.162 0.163 0.163
##  [7309] 0.163 0.163 0.164 0.162 0.163 0.163 0.163 0.163 0.164 0.162 0.162 0.163
##  [7321] 0.163 0.166 0.164 0.162 0.162 0.163 0.163 0.164 0.164 0.162 0.162 0.163
##  [7333] 0.163 0.167 0.164 0.162 0.165 0.163 0.163 0.163 0.164 0.162 0.162 0.163
##  [7345] 0.163 0.163 0.164 0.162 0.162 0.163 0.163 0.165 0.165 0.162 0.162 0.163
##  [7357] 0.163 0.163 0.164 0.162 0.163 0.163 0.163 0.164 0.162 0.163 0.163 0.165
##  [7369] 0.164 0.162 0.163 0.163 0.164 0.164 0.162 0.163 0.165 0.164 0.162 0.163
##  [7381] 0.163 0.164 0.162 0.163 0.163 0.164 0.162 0.163 0.163 0.164 0.162 0.163
##  [7393] 0.164 0.165 0.162 0.163 0.163 0.162 0.164 0.162 0.163 0.163 0.162 0.163
##  [7405] 0.162 0.164 0.162 0.163 0.162 0.162 0.163 0.162 0.162 0.163 0.164 0.162
##  [7417] 0.164 0.162 0.163 0.165 0.162 0.164 0.163 0.163 0.164 0.162 0.164 0.162
##  [7429] 0.163 0.165 0.162 0.164 0.162 0.163 0.164 0.162 0.164 0.162 0.163 0.163
##  [7441] 0.163 0.164 0.164 0.162 0.163 0.163 0.163 0.164 0.164 0.162 0.163 0.163
##  [7453] 0.163 0.164 0.164 0.162 0.163 0.163 0.164 0.164 0.164 0.162 0.163 0.163
##  [7465] 0.164 0.164 0.164 0.162 0.163 0.163 0.163 0.164 0.164 0.162 0.163 0.163
##  [7477] 0.163 0.164 0.164 0.164 0.163 0.163 0.163 0.164 0.164 0.162 0.163 0.163
##  [7489] 0.163 0.164 0.164 0.162 0.163 0.163 0.163 0.164 0.164 0.162 0.163 0.163
##  [7501] 0.163 0.166 0.164 0.162 0.163 0.163 0.165 0.164 0.164 0.162 0.163 0.163
##  [7513] 0.163 0.164 0.164 0.162 0.163 0.163 0.163 0.164 0.165 0.165 0.163 0.163
##  [7525] 0.164 0.163 0.186 0.163 0.163 0.167 0.163 0.193 0.163 0.165 0.187 0.181
##  [7537] 0.180 0.163 0.163 0.177 0.162 0.164 0.163 0.163 0.163 0.170 0.163 0.163
##  [7549] 0.163 0.208 0.174 0.166 0.163 0.189 0.173 0.167 0.175 0.163 0.163 0.169
##  [7561] 0.174 0.170 0.163 0.163 0.167 0.182 0.166 0.163 0.163 0.170 0.163 0.171
##  [7573] 0.163 0.163 0.195 0.169 0.163 0.163 0.163 0.163 0.171 0.163 0.163 0.173
##  [7585] 0.163 0.167 0.162 0.163 0.163 0.166 0.164 0.162 0.163 0.163 0.164 0.162
##  [7597] 0.164 0.163 0.163 0.189 0.172 0.168 0.163 0.163 0.165 0.181 0.162 0.163
##  [7609] 0.163 0.166 0.164 0.169 0.162 0.163 0.163 0.165 0.163 0.171 0.163 0.163
##  [7621] 0.192 0.167 0.168 0.164 0.163 0.163 0.163 0.163 0.163 0.163 0.165 0.185
##  [7633] 0.163 0.163 0.163 0.180 0.163 0.165 0.230 0.163 0.176 0.164 0.243 0.164
##  [7645] 0.163 0.165 0.267 0.164 0.235 0.228 0.173 0.165 0.193 0.164 0.233 0.187
##  [7657] 0.163 0.165 0.268 0.176 0.260 0.181 0.163 0.165 0.260 0.164 0.217 0.177
##  [7669] 0.165 0.246 0.164 0.238 0.167 0.186 0.204 0.164 0.232 0.170 0.178 0.238
##  [7681] 0.164 0.196 0.177 0.197 0.211 0.164 0.206 0.185 0.173 0.178 0.164 0.175
##  [7693] 0.176 0.248 0.162 0.164 0.181 0.169 0.167 0.162 0.164 0.163 0.167 0.163
##  [7705] 0.162 0.171 0.165 0.163 0.162 0.163 0.168 0.165 0.163 0.162 0.163 0.169
##  [7717] 0.163 0.163 0.200 0.163 0.162 0.163 0.163 0.226 0.163 0.163 0.163 0.163
##  [7729] 0.184 0.163 0.170 0.163 0.163 0.184 0.163 0.165 0.163 0.163 0.187 0.163
##  [7741] 0.165 0.163 0.163 0.163 0.163 0.164 0.163 0.163 0.163 0.163 0.164 0.163
##  [7753] 0.163 0.163 0.163 0.162 0.163 0.163 0.168 0.164 0.162 0.163 0.163 0.176
##  [7765] 0.163 0.162 0.163 0.163 0.176 0.163 0.170 0.163 0.173 0.180 0.163 0.231
##  [7777] 0.163 0.164 0.170 0.163 0.182 0.163 0.163 0.173 0.163 0.190 0.163 0.163
##  [7789] 0.163 0.165 0.163 0.239 0.253 0.251 0.172 0.163 0.255 0.254 0.242 0.174
##  [7801] 0.164 0.224 0.259 0.239 0.251 0.225 0.180 0.253 0.211 0.207 0.275 0.163
##  [7813] 0.197 0.252 0.203 0.230 0.237 0.212 0.260 0.211 0.242 0.268 0.164 0.223
##  [7825] 0.248 0.256 0.236 0.250 0.172 0.262 0.241 0.234 0.263 0.164 0.162 0.237
##  [7837] 0.238 0.220 0.228 0.174 0.166 0.237 0.208 0.215 0.277 0.164 0.199 0.244
##  [7849] 0.222 0.259 0.275 0.163 0.244 0.247 0.215 0.185 0.162 0.182 0.248 0.253
##  [7861] 0.173 0.164 0.220 0.215 0.255 0.175 0.175 0.214 0.220 0.244 0.176 0.162
##  [7873] 0.203 0.197 0.199 0.174 0.167 0.217 0.185 0.199 0.169 0.168 0.263 0.205
##  [7885] 0.243 0.182 0.163 0.253 0.259 0.253 0.181 0.162 0.164 0.239 0.238 0.179
##  [7897] 0.164 0.164 0.164 0.250 0.258 0.173 0.163 0.164 0.252 0.278 0.171 0.162
##  [7909] 0.164 0.235 0.165 0.204 0.164 0.166 0.164 0.237 0.165 0.180 0.164 0.187
##  [7921] 0.408 0.246 0.165 0.205 0.164 0.191 0.309 0.232 0.165 0.185 0.164 0.201
##  [7933] 0.217 0.236 0.165 0.269 0.164 0.207 0.220 0.220 0.165 0.212 0.164 0.199
##  [7945] 0.234 0.165 0.214 0.164 0.215 0.234 0.210 0.180 0.164 0.205 0.225 0.247
##  [7957] 0.216 0.164 0.233 0.244 0.225 0.187 0.164 0.220 0.238 0.239 0.241 0.164
##  [7969] 0.226 0.256 0.223 0.184 0.164 0.166 0.242 0.237 0.178 0.181 0.240 0.235
##  [7981] 0.196 0.187 0.165 0.210 0.182 0.163 0.194 0.252 0.237 0.167 0.189 0.163
##  [7993] 0.180 0.258 0.224 0.251 0.163 0.208 0.258 0.232 0.275 0.163 0.209 0.247
##  [8005] 0.212 0.270 0.164 0.163 0.186 0.251 0.243 0.267 0.164 0.163 0.252 0.253
##  [8017] 0.221 0.261 0.164 0.163 0.246 0.257 0.228 0.249 0.163 0.254 0.241 0.186
##  [8029] 0.271 0.164 0.163 0.170 0.221 0.225 0.237 0.164 0.163 0.188 0.260 0.219
##  [8041] 0.243 0.164 0.201 0.244 0.253 0.194 0.163 0.173 0.244 0.248 0.245 0.164
##  [8053] 0.163 0.174 0.252 0.261 0.272 0.164 0.163 0.232 0.240 0.239 0.243 0.163
##  [8065] 0.252 0.236 0.237 0.212 0.163 0.238 0.242 0.223 0.163 0.178 0.177 0.172
##  [8077] 0.163 0.181 0.186 0.163 0.176 0.179 0.163 0.163 0.177 0.165 0.178 0.177
##  [8089] 0.163 0.181 0.178 0.163 0.185 0.174 0.163 0.181 0.177 0.163 0.180 0.180
##  [8101] 0.179 0.178 0.163 0.176 0.179 0.163 0.177 0.178 0.163 0.184 0.182 0.164
##  [8113] 0.187 0.176 0.165 0.185 0.179 0.163 0.182 0.182 0.163 0.178 0.178 0.165
##  [8125] 0.181 0.182 0.163 0.180 0.177 0.163 0.178 0.179 0.165 0.182 0.177 0.163
##  [8137] 0.174 0.176 0.165 0.181 0.178 0.163 0.176 0.175 0.163 0.175 0.177 0.163
##  [8149] 0.173 0.175 0.164 0.177 0.181 0.163 0.164 0.174 0.164 0.165 0.179 0.163
##  [8161] 0.164 0.178 0.192 0.164 0.184 0.164 0.178 0.164 0.166 0.176 0.188 0.174
##  [8173] 0.168 0.174 0.178 0.173 0.173 0.177 0.174 0.173 0.175 0.175 0.171 0.177
##  [8185] 0.174 0.181 0.176 0.179 0.174 0.174 0.166 0.182 0.182 0.175 0.186 0.171
##  [8197] 0.173 0.173 0.174 0.174 0.169 0.175 0.176 0.178 0.187 0.178 0.176 0.165
##  [8209] 0.163 0.164 0.163 0.164 0.163 0.164 0.163 0.163 0.165 0.164 0.163 0.164
##  [8221] 0.163 0.164 0.163 0.164 0.163 0.162 0.163 0.164 0.163 0.165 0.163 0.164
##  [8233] 0.163 0.164 0.164 0.164 0.165 0.164 0.163 0.164 0.163 0.164 0.165 0.164
##  [8245] 0.163 0.164 0.163 0.164 0.165 0.164 0.163 0.164 0.165 0.164 0.163 0.164
##  [8257] 0.163 0.164 0.163 0.164 0.164 0.169 0.163 0.164 0.164 0.194 0.163 0.162
##  [8269] 0.162 0.162 0.164 0.163 0.165 0.162 0.164 0.162 0.165 0.162 0.164 0.162
##  [8281] 0.163 0.164 0.163 0.164 0.163 0.164 0.164 0.164 0.164 0.164 0.163 0.164
##  [8293] 0.163 0.164 0.163 0.164 0.163 0.164 0.163 0.165 0.163 0.164 0.165 0.165
##  [8305] 0.163 0.164 0.163 0.164 0.165 0.167 0.165 0.163 0.163 0.169 0.177 0.179
##  [8317] 0.163 0.163 0.167 0.171 0.174 0.163 0.163 0.180 0.179 0.164 0.163 0.163
##  [8329] 0.163 0.186 0.169 0.163 0.165 0.167 0.185 0.164 0.163 0.163 0.165 0.181
##  [8341] 0.165 0.163 0.163 0.172 0.176 0.187 0.163 0.163 0.166 0.177 0.189 0.163
##  [8353] 0.163 0.173 0.178 0.183 0.163 0.163 0.175 0.183 0.185 0.163 0.163 0.181
##  [8365] 0.190 0.170 0.163 0.163 0.178 0.185 0.189 0.163 0.163 0.185 0.185 0.190
##  [8377] 0.163 0.164 0.189 0.166 0.185 0.163 0.165 0.187 0.180 0.182 0.163 0.188
##  [8389] 0.186 0.188 0.163 0.176 0.187 0.182 0.165 0.179 0.180 0.180 0.163 0.178
##  [8401] 0.177 0.164 0.163 0.178 0.180 0.164 0.165 0.179 0.186 0.164 0.163 0.176
##  [8413] 0.187 0.164 0.165 0.183 0.197 0.164 0.163 0.178 0.182 0.162 0.163 0.183
##  [8425] 0.200 0.188 0.163 0.326 0.215 0.182 0.253 0.321 0.304 0.250 0.238 0.251
##  [8437] 0.333 0.212 0.205 0.297 0.267 0.183 0.248 0.292 0.217 0.319 0.274 0.210
##  [8449] 0.256 0.314 0.228 0.169 0.262 0.189 0.164 0.187 0.164 0.182 0.189 0.187
##  [8461] 0.165 0.184 0.179 0.188 0.164 0.187 0.181 0.184 0.165 0.184 0.181 0.188
##  [8473] 0.164 0.189 0.181 0.191 0.163 0.163 0.187 0.183 0.178 0.163 0.163 0.190
##  [8485] 0.176 0.183 0.163 0.163 0.188 0.177 0.191 0.163 0.164 0.186 0.182 0.192
##  [8497] 0.163 0.164 0.186 0.181 0.189 0.163 0.163 0.186 0.184 0.191 0.163 0.163
##  [8509] 0.185 0.183 0.164 0.163 0.163 0.189 0.187 0.164 0.163 0.163 0.179 0.183
##  [8521] 0.174 0.163 0.163 0.177 0.175 0.173 0.163 0.163 0.182 0.173 0.187 0.163
##  [8533] 0.165 0.178 0.182 0.188 0.163 0.163 0.172 0.168 0.177 0.163 0.163 0.169
##  [8545] 0.178 0.165 0.163 0.168 0.165 0.163 0.163 0.163 0.163 0.165 0.163 0.163
##  [8557] 0.163 0.163 0.163 0.163 0.163 0.163 0.164 0.165 0.163 0.163 0.257 0.164
##  [8569] 0.165 0.164 0.165 0.164 0.163 0.163 0.163 0.164 0.164 0.163 0.163 0.163
##  [8581] 0.163 0.163 0.163 0.165 0.163 0.163 0.164 0.164 0.164 0.164 0.164 0.164
##  [8593] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
##  [8605] 0.163 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
##  [8617] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
##  [8629] 0.163 0.164 0.164 0.163 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
##  [8641] 0.164 0.164 0.164 0.164 0.163 0.164 0.164 0.164 0.164 0.164 0.164 0.164
##  [8653] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
##  [8665] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
##  [8677] 0.164 0.164 0.164 0.164 0.164 0.164 0.163 0.164 0.164 0.164 0.164 0.164
##  [8689] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
##  [8701] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
##  [8713] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
##  [8725] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
##  [8737] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
##  [8749] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.163 0.164 0.164
##  [8761] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.163 0.163 0.164 0.164 0.164
##  [8773] 0.164 0.164 0.164 0.163 0.164 0.164 0.287 0.358 0.164 0.268 0.164 0.164
##  [8785] 0.164 0.164 0.164 0.164 0.163 0.164 0.164 0.176 0.172 0.170 0.167 0.165
##  [8797] 0.166 0.167 0.165 0.167 0.168 0.166 0.167 0.166 0.166 0.167 0.167 0.168
##  [8809] 0.169 0.168 0.169 0.166 0.166 0.167 0.167 0.166 0.166 0.167 0.167 0.166
##  [8821] 0.167 0.167 0.169 0.167 0.167 0.166 0.168 0.167 0.174 0.166 0.169 0.168
##  [8833] 0.167 0.166 0.167 0.168 0.167 0.168 0.167 0.168 0.167 0.165 0.165 0.166
##  [8845] 0.168 0.168 0.166 0.166 0.166 0.166 0.167 0.166 0.167 0.167 0.165 0.167
##  [8857] 0.167 0.166 0.166 0.168 0.167 0.169 0.166 0.166 0.167 0.166 0.166 0.165
##  [8869] 0.166 0.166 0.166 0.168 0.167 0.166 0.166 0.168 0.170 0.169 0.165 0.167
##  [8881] 0.168 0.169 0.169 0.166 0.166 0.166 0.167 0.169 0.167 0.168 0.167 0.163
##  [8893] 0.164 0.166 0.166 0.165 0.168 0.172 0.167 0.167 0.167 0.166 0.171 0.170
##  [8905] 0.169 0.170 0.166 0.164 0.165 0.165 0.165 0.165 0.165 0.166 0.166 0.165
##  [8917] 0.166 0.166 0.166 0.166 0.166 0.165 0.165 0.168 0.181 0.178 0.164 0.168
##  [8929] 0.167 0.166 0.166 0.165 0.166 0.166 0.166 0.165 0.166 0.166 0.166 0.165
##  [8941] 0.165 0.168 0.167 0.167 0.169 0.167 0.166 0.167 0.167 0.166 0.167 0.166
##  [8953] 0.166 0.166 0.166 0.166 0.177 0.166 0.167 0.166 0.168 0.168 0.167 0.166
##  [8965] 0.169 0.168 0.170 0.169 0.167 0.168 0.167 0.168 0.169 0.169 0.167 0.167
##  [8977] 0.167 0.166 0.170 0.173 0.173 0.167 0.167 0.167 0.167 0.167 0.168 0.167
##  [8989] 0.170 0.167 0.170 0.170 0.168 0.168 0.167 0.169 0.167 0.167 0.166 0.167
##  [9001] 0.164 0.166 0.167 0.167 0.165 0.165 0.164 0.164 0.164 0.166 0.166 0.167
##  [9013] 0.167 0.165 0.163 0.164 0.165 0.165 0.165 0.164 0.166 0.164 0.164 0.165
##  [9025] 0.165 0.165 0.166 0.165 0.166 0.165 0.165 0.166 0.165 0.165 0.164 0.165
##  [9037] 0.165 0.166 0.166 0.165 0.166 0.165 0.169 0.233 0.165 0.165 0.165 0.165
##  [9049] 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.166 0.166 0.166
##  [9061] 0.165 0.165 0.166 0.165 0.166 0.166 0.165 0.166 0.165 0.163 0.165 0.165
##  [9073] 0.165 0.166 0.165 0.166 0.166 0.166 0.166 0.165 0.166 0.166 0.166 0.165
##  [9085] 0.166 0.166 0.166 0.165 0.164 0.164 0.166 0.166 0.165 0.166 0.166 0.165
##  [9097] 0.165 0.165 0.165 0.165 0.166 0.165 0.166 0.166 0.165 0.165 0.163 0.165
##  [9109] 0.165 0.165 0.165 0.164 0.165 0.165 0.166 0.165 0.165 0.166 0.166 0.165
##  [9121] 0.165 0.165 0.166 0.165 0.165 0.165 0.169 0.166 0.169 0.165 0.167 0.168
##  [9133] 0.167 0.165 0.166 0.165 0.165 0.165 0.167 0.166 0.169 0.175 0.178 0.174
##  [9145] 0.168 0.173 0.176 0.175 0.176 0.177 0.167 0.167 0.165 0.165 0.167 0.166
##  [9157] 0.167 0.167 0.167 0.167 0.166 0.165 0.165 0.165 0.166 0.165 0.165 0.165
##  [9169] 0.166 0.165 0.165 0.165 0.165 0.165 0.165 0.164 0.164 0.166 0.165 0.165
##  [9181] 0.165 0.165 0.166 0.166 0.166 0.165 0.165 0.165 0.165 0.165 0.165 0.165
##  [9193] 0.165 0.165 0.165 0.167 0.165 0.165 0.165 0.165 0.164 0.165 0.166 0.165
##  [9205] 0.165 0.165 0.165 0.165 0.163 0.165 0.165 0.164 0.165 0.164 0.165 0.165
##  [9217] 0.165 0.165 0.165 0.164 0.165 0.165 0.165 0.164 0.165 0.165 0.164 0.164
##  [9229] 0.166 0.167 0.165 0.164 0.168 0.171 0.168 0.167 0.174 0.168 0.176 0.182
##  [9241] 0.173 0.169 0.170 0.181 0.172 0.169 0.169 0.168 0.164 0.168 0.165 0.168
##  [9253] 0.166 0.166 0.168 0.168 0.166 0.167 0.171 0.169 0.167 0.166 0.171 0.165
##  [9265] 0.165 0.165 0.174 0.165 0.170 0.164 0.169 0.165 0.167 0.170 0.166 0.169
##  [9277] 0.170 0.172 0.168 0.177 0.182 0.175 0.176 0.175 0.168 0.165 0.168 0.165
##  [9289] 0.166 0.166 0.167 0.165 0.164 0.169 0.166 0.167 0.167 0.165 0.165 0.165
##  [9301] 0.165 0.165 0.164 0.165 0.165 0.165 0.165 0.177 0.165 0.182 0.167 0.178
##  [9313] 0.175 0.179 0.175 0.167 0.167 0.182 0.165 0.165 0.165 0.165 0.165 0.167
##  [9325] 0.165 0.165 0.164 0.166 0.170 0.166 0.166 0.168 0.166 0.166 0.167 0.167
##  [9337] 0.166 0.171 0.165 0.164 0.166 0.164 0.168 0.168 0.173 0.171 0.169 0.166
##  [9349] 0.166 0.167 0.170 0.165 0.171 0.171 0.166 0.165 0.165 0.166 0.167 0.167
##  [9361] 0.166 0.170 0.166 0.167 0.164 0.167 0.170 0.166 0.167 0.172 0.164 0.165
##  [9373] 0.174 0.172 0.166 0.166 0.174 0.174 0.165 0.166 0.166 0.165 0.167 0.164
##  [9385] 0.167 0.164 0.168 0.167 0.166 0.169 0.166 0.168 0.165 0.168 0.171 0.167
##  [9397] 0.166 0.166 0.166 0.165 0.167 0.167 0.164 0.167 0.168 0.165 0.166 0.169
##  [9409] 0.165 0.167 0.170 0.165 0.165 0.165 0.165 0.167 0.164 0.166 0.165 0.165
##  [9421] 0.165 0.164 0.165 0.165 0.164 0.180 0.180 0.217 0.231 0.238 0.228 0.247
##  [9433] 0.289 0.228 0.179 0.165 0.165 0.165 0.164 0.164 0.165 0.164 0.165 0.165
##  [9445] 0.165 0.165 0.165 0.167 0.166 0.168 0.166 0.166 0.166 0.166 0.164 0.164
##  [9457] 0.164 0.162 0.162 0.162 0.162 0.162 0.164 0.164 0.214 0.203 0.198 0.210
##  [9469] 0.173 0.165 0.186 0.178 0.165 0.175 0.167 0.167 0.173 0.174 0.170 0.169
##  [9481] 0.165 0.166 0.164 0.165 0.165 0.165 0.165 0.165 0.165 0.164 0.164 0.164
##  [9493] 0.164 0.164 0.165 0.164 0.164 0.168 0.166 0.167 0.166 0.166 0.165 0.165
##  [9505] 0.165 0.164 0.166 0.165 0.166 0.165 0.167 0.166 0.166 0.166 0.164 0.223
##  [9517] 0.204 0.195 0.183 0.205 0.200 0.178 0.167 0.168 0.166 0.169 0.172 0.168
##  [9529] 0.168 0.168 0.168 0.168 0.171 0.176 0.171 0.190 0.183 0.181 0.186 0.196
##  [9541] 0.190 0.191 0.184 0.183 0.184 0.185 0.186 0.185 0.184 0.185 0.179 0.176
##  [9553] 0.183 0.178 0.172 0.178 0.172 0.179 0.176 0.174 0.174 0.170 0.176 0.171
##  [9565] 0.170 0.169 0.174 0.171 0.173 0.172 0.170 0.168 0.167 0.169 0.179 0.164
##  [9577] 0.168 0.180 0.165 0.168 0.165 0.174 0.169 0.171 0.165 0.165 0.175 0.165
##  [9589] 0.182 0.178 0.168 0.163 0.182 0.164 0.164 0.181 0.185 0.177 0.178 0.181
##  [9601] 0.176 0.175 0.172 0.176 0.177 0.166 0.177 0.183 0.181 0.184 0.181 0.179
##  [9613] 0.182 0.185 0.178 0.168 0.166 0.165 0.214 0.235 0.244 0.288 0.329 0.317
##  [9625] 0.246 0.238 0.321 0.228 0.299 0.274 0.307 0.241 0.278 0.290 0.271 0.272
##  [9637] 0.238 0.424 0.485 0.500 0.509 0.427 0.463 0.462 0.429 0.371 0.330 0.412
##  [9649] 0.257 0.241 0.228 0.279 0.284 0.358 0.164 0.164 0.164 0.164 0.164 0.165
##  [9661] 0.198 0.368 0.165 0.165 0.485 0.886 0.556 0.570 0.393 0.269 0.194 0.194
##  [9673] 0.191 0.189 0.190 0.180 0.189 0.187 0.189 0.192 0.186 0.194 0.190 0.196
##  [9685] 0.296 0.296 0.305 0.323 0.250 0.170 0.165 0.167 0.174 0.169 0.186 0.193
##  [9697] 0.180 0.165 0.187 0.190 0.181 0.168 0.173 0.171 0.165 0.165 0.167 0.172
##  [9709] 0.164 0.164 0.164 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.170
##  [9721] 0.164 0.166 0.164 0.165 0.170 0.171 0.165 0.165 0.164 0.163 0.166 0.165
##  [9733] 0.164 0.183 0.164 0.167 0.173 0.173 0.168 0.255 0.186 0.197 0.181 0.194
##  [9745] 0.167 0.169 0.166 0.164 0.171 0.178 0.179 0.166 0.167 0.166 0.236 0.216
##  [9757] 0.240 0.235 0.178 0.286 0.270 0.283 0.227 0.178 0.175 0.189 0.243 0.172
##  [9769] 0.178 0.173 0.177 0.175 0.184 0.231 0.179 0.194 0.209 0.166 0.180 0.171
##  [9781] 0.170 0.177 0.173 0.173 0.182 0.177 0.174 0.174 0.187 0.181 0.169 0.211
##  [9793] 0.169 0.197 0.181 0.166 0.185 0.167 0.198 0.166 0.206 0.167 0.204 0.166
##  [9805] 0.217 0.166 0.167 0.215 0.264 0.285 0.286 0.282 0.277 0.286 0.276 0.257
##  [9817] 0.258 0.307 0.181 0.187 0.175 0.170 0.175 0.171 0.177 0.181 0.168 0.170
##  [9829] 0.169 0.168 0.169 0.167 0.170 0.171 0.169 0.169 0.171 0.169 0.170 0.169
##  [9841] 0.165 0.404 0.240 0.232 0.293 0.240 0.303 0.320 0.322 0.241 0.244 0.212
##  [9853] 0.238 0.290 0.242 0.223 0.207 0.199 0.252 0.255 0.241 0.267 0.285 0.243
##  [9865] 0.274 0.213 0.209 0.222 0.253 0.236 0.191 0.208 0.247 0.215 0.172 0.170
##  [9877] 0.209 0.265 0.363 0.175 0.277 0.314 0.344 0.247 0.293 0.239 0.243 0.241
##  [9889] 0.326 0.337 0.337 0.384 0.286 0.228 0.328 0.179 0.180 0.285 0.168 0.176
##  [9901] 0.169 0.319 0.263 0.167 0.293 0.334 0.289 0.302 0.220 0.224 0.176 0.217
##  [9913] 0.230 0.247 0.203 0.218 0.209 0.222 0.245 0.241 0.231 0.185 0.170 0.172
##  [9925] 0.290 0.198 0.266 0.212 0.238 0.235 0.168 0.181 0.288 0.273 0.302 0.286
##  [9937] 0.234 0.266 0.214 0.207 0.164 0.297 0.243 0.222 0.173 0.175 0.166 0.200
##  [9949] 0.194 0.191 0.224 0.226 0.189 0.191 0.194 0.176 0.174 0.165 0.171 0.176
##  [9961] 0.177 0.167 0.178 0.166 0.176 0.173 0.176 0.164 0.164 0.164 0.164 0.164
##  [9973] 0.164 0.282 0.296 0.243 0.316 0.230 0.257 0.299 0.396 0.343 0.327 0.317
##  [9985] 0.241 0.275 0.257 0.296 0.261 0.296 0.301 0.280 0.277 0.331 0.287 0.274
##  [9997] 0.360 0.322 0.260 0.256 0.303 0.283 0.272 0.189 0.177 0.180 0.206 0.193
## [10009] 0.200 0.192 0.189 0.189 0.188 0.178 0.185 0.205 0.222 0.226 0.188 0.178
## [10021] 0.186 0.196 0.210 0.199 0.189 0.214 0.223 0.219 0.217 0.197 0.191 0.200
## [10033] 0.230 0.191 0.197 0.217 0.216 0.164 0.265 0.243 0.223 0.217 0.189 0.217
## [10045] 0.208 0.246 0.164 0.171 0.170 0.168 0.284 0.266 0.528 0.260 0.396 0.384
## [10057] 0.274 0.207 0.390 0.238 0.210 0.227 0.217 0.205 0.301 0.238 0.262 0.232
## [10069] 0.240 0.263 0.234 0.238 0.253 0.243 0.245 0.222 0.239 0.206 0.164 0.232
## [10081] 0.163 0.235 0.164 0.208 0.163 0.228 0.164 0.236 0.165 0.267 0.164 0.217
## [10093] 0.163 0.226 0.162 0.235 0.164 0.267 0.164 0.378 0.162 0.250 0.163 0.225
## [10105] 0.163 0.562 0.164 0.386 0.165 0.370 0.165 0.408 0.165 0.355 0.165 0.396
## [10117] 0.165 0.351 0.370 0.173 0.184 0.177 0.179 0.192 0.191 0.317 0.214 0.207
## [10129] 0.214 0.254 0.299 0.221 0.285 0.313 0.310 0.217 0.327 0.241 0.233 0.236
## [10141] 0.215 0.167 0.173 0.261 0.238 0.376 0.395 0.332 0.321 0.322 0.358 0.252
## [10153] 0.182 0.195 0.248 0.258 0.202 0.292 0.333 0.320 0.185 0.181 0.255 0.230
## [10165] 0.205 0.275 0.252 0.224 0.203 0.474 0.338 0.361 0.249 0.241 0.268 0.227
## [10177] 0.177 0.179 0.175 0.173 0.229 0.325 0.345 0.327 0.209 0.191 0.177 0.172
## [10189] 0.177 0.165 0.165 0.165 0.165 0.165 0.234 0.252 0.256 0.164 0.199 0.165
## [10201] 0.207 0.165 0.234 0.163 0.266 0.164 0.294 0.164 0.307 0.314 0.245 0.256
## [10213] 0.323 0.343 0.310 0.254 0.164 0.169 0.164 0.194 0.223 0.176 0.165 0.162
## [10225] 0.165 0.163 0.164 0.164 0.165 0.165 0.165 0.163 0.165 0.164 0.165 0.164
## [10237] 0.165 0.164 0.165 0.164 0.165 0.164 0.164 0.165 0.166 0.199 0.215 0.196
## [10249] 0.179 0.186 0.167 0.179 0.253 0.194 0.165 0.275 0.214 0.274 0.169 0.193
## [10261] 0.196 0.190 0.205 0.170 0.183 0.180 0.168 0.181 0.171 0.185 0.239 0.178
## [10273] 0.191 0.164 0.177 0.178 0.173 0.167 0.169 0.179 0.183 0.171 0.178 0.169
## [10285] 0.173 0.173 0.172 0.177 0.172 0.188 0.174 0.202 0.171 0.182 0.176 0.192
## [10297] 0.175 0.189 0.173 0.180 0.180 0.186 0.167 0.184 0.170 0.180 0.179 0.184
## [10309] 0.175 0.221 0.178 0.168 0.168 0.168 0.180 0.170 0.166 0.170 0.168 0.166
## [10321] 0.164 0.165 0.167 0.164 0.169 0.176 0.165 0.166 0.167 0.169 0.169 0.176
## [10333] 0.172 0.181 0.173 0.174 0.172 0.176 0.176 0.207 0.232 0.306 0.324 0.248
## [10345] 0.253 0.275 0.195 0.245 0.214 0.284 0.379 0.275 0.323 0.326 0.372 0.347
## [10357] 0.339 0.325 0.228 0.542 0.365 0.317 0.301 0.336 0.339 0.359 0.249 0.313
## [10369] 0.233 0.196 0.297 0.275 0.185 0.220 0.259 0.435 0.210 0.243 0.187 0.276
## [10381] 0.322 0.353 0.431 0.240 0.371 0.449 0.402 0.427 0.439 0.492 0.459 0.340
## [10393] 0.362 0.316 0.255 0.321 0.343 0.336 0.356 0.357 0.342 0.367 0.352 0.233
## [10405] 0.297 0.204 0.246 0.285 0.377 0.207 0.173 0.169 0.166 0.165 0.166 0.167
## [10417] 0.170 0.165 0.166 0.185 0.164 0.216 0.162 0.255 0.188 0.218 0.197 0.191
## [10429] 0.177 0.192 0.370 0.375 0.167 0.239 0.288 0.305 0.184 0.208 0.183 0.165
## [10441] 0.168 0.169 0.168 0.170 0.185 0.181 0.171 0.192 0.196 0.174 0.174 0.167
## [10453] 0.165 0.164 0.423 0.326 0.259 0.217 0.324 0.301 0.232 0.228 0.305 0.367
## [10465] 0.234 0.232 0.223 0.285 0.266 0.386 0.268 0.209 0.214 0.193 0.221 0.300
## [10477] 0.315 0.296 0.245 0.240 0.351 0.212 0.188 0.199 0.203 0.182 0.201 0.222
## [10489] 0.281 0.288 0.213 0.240 0.258 0.259 0.259 0.235 0.270 0.186 0.252 0.298
## [10501] 0.298 0.268 0.257 0.201 0.203 0.230 0.250 0.238 0.222 0.216 0.162 0.169
## [10513] 0.173 0.163 0.166 0.226 0.162 0.162 0.163 0.254 0.164 0.164 0.162 0.163
## [10525] 0.163 0.163 0.162 0.164 0.164 0.192 0.164 0.162 0.162 0.319 0.379 0.380
## [10537] 0.214 0.167 0.166 0.201 0.176 0.181 0.376 0.395 0.482 0.228 0.175 0.164
## [10549] 0.164 0.163 0.162 0.165 0.163 0.181 0.266 0.181 0.162 0.165 0.165 0.172
## [10561] 0.172 0.184 0.169 0.169 0.175 0.179 0.197 0.190 0.193 0.232 0.203 0.185
## [10573] 0.213 0.177 0.195 0.173 0.169 0.172 0.195 0.212 0.215 0.217 0.244 0.318
## [10585] 0.324 0.270 0.194 0.180 0.176 0.183 0.186 0.223 0.191 0.216 0.333 0.220
## [10597] 0.186 0.198 0.179 0.228 0.253 0.214 0.265 0.228 0.245 0.303 0.183 0.229
## [10609] 0.229 0.396 0.377 0.451 0.727 0.636 0.354 0.359 0.263 0.222 0.289 0.308
## [10621] 0.289 0.290 0.193 0.224 0.237 0.244 0.236 0.217 0.162 0.162 0.162 0.162
## [10633] 0.162 0.164 0.163 0.164 0.162 0.162 0.162 0.162 0.162 0.162 0.162 0.162
## [10645] 0.162 0.162 0.162 0.199 0.164 0.180 0.165 0.203 0.164 0.206 0.162 0.230
## [10657] 0.162 0.368 0.164 0.387 0.164 0.769 0.164 0.364 0.164 0.272 0.164 0.216
## [10669] 0.165 0.331 0.164 0.396 0.164 0.275 0.164 0.215 0.165 0.221 0.164 0.206
## [10681] 0.163 0.360 0.164 0.237 0.164 0.341 0.164 0.162 0.165 0.165 0.164 0.164
## [10693] 0.164 0.162 0.164 0.164 0.165 0.164 0.164 0.164 0.164 0.164 0.164 0.162
## [10705] 0.164 0.164 0.164 0.162 0.162 0.162 0.202 0.266 0.373 0.436 0.609 0.319
## [10717] 0.440 0.432 0.703 0.534 0.489 0.706 0.478 0.867 0.164 0.210 0.273 0.295
## [10729] 0.206 0.260 0.185 0.243 0.239 0.182 0.247 0.234 0.296 0.206 0.226 0.250
## [10741] 0.235 0.378 0.379 0.230 0.312 0.224 0.225 0.338 0.546 0.332 0.309 0.296
## [10753] 0.263 0.377 0.264 0.465 0.497 0.423 0.495 0.514 0.449 0.286 0.399 0.437
## [10765] 0.370 0.269 0.381 0.352 0.164 0.164 0.164 0.371 0.266 0.164 0.275 0.173
## [10777] 0.242 0.181 0.189 0.182 0.164 0.164 0.165 0.206 0.235 0.164 0.265 0.239
## [10789] 0.214 0.250 0.255 0.463 0.254 0.261 0.213 0.217 0.207 0.204 0.202 0.212
## [10801] 0.224 0.224 0.247 0.252 0.256 0.292 0.296 0.227 0.164 0.179 0.164 0.164
## [10813] 0.171 0.309 0.375 0.266 0.597 0.323 0.202 0.334 0.261 0.455 0.403 0.352
## [10825] 0.423 0.369 0.282 0.437 0.359 0.283 0.222 0.213 0.233 0.261 0.165 0.165
## [10837] 0.165 0.175 0.173 0.335 0.214 0.202 0.221 0.331 0.292 0.380 0.750 0.641
## [10849] 0.421 0.460 0.319 0.572 0.200 0.446 0.461 0.448 0.392 0.283 0.255 0.238
## [10861] 0.340 0.257 0.213 0.165 0.165 0.165 0.165 0.165 0.196 0.183 0.269 0.275
## [10873] 0.249 0.625 0.267 0.244 0.243 0.225 0.287 0.271 0.271 0.260 0.283 0.268
## [10885] 0.306 0.209 0.511 0.752 0.422 0.212 0.190 0.193 0.266 0.200 0.328 0.213
## [10897] 0.689 0.367 0.464 0.217 0.570 0.221 0.390 0.303 0.399 0.196 0.377 0.327
## [10909] 0.331 0.242 0.508 0.291 0.569 0.219 0.413 0.263 0.508 0.199 0.429 0.239
## [10921] 0.533 0.167 0.529 0.213 0.435 0.182 0.595 0.562 0.562 0.256 0.335 0.325
## [10933] 0.357 0.333 0.302 0.247 0.213 0.398 0.373 0.277 0.165 0.164 0.172 0.164
## [10945] 0.218 0.190 0.167 0.183 0.211 0.164 0.164 0.194 0.327 0.216 0.367 0.226
## [10957] 0.213 0.235 0.235 0.239 0.259 0.227 0.317 0.236 0.337 0.185 0.239 0.243
## [10969] 0.230 0.272 0.323 0.318 0.305 0.288 0.251 0.260 0.243 0.244 0.353 0.197
## [10981] 0.233 0.176 0.184 0.183 0.198 0.175 0.232 0.168 0.183 0.171 0.167 0.235
## [10993] 0.241 0.270 0.170 0.315 0.164 0.263 0.262 0.205 0.216 0.166 0.168 0.165
## [11005] 0.170 0.196 0.251 0.408 0.413 0.475 0.688 0.394 0.500 0.434 0.489 0.472
## [11017] 0.440 0.623 0.457 0.470 0.488 0.408 0.417 0.499 0.458 0.520 0.449 0.469
## [11029] 0.453 0.210 0.266 0.245 0.221 0.221 0.198 0.357 0.236 0.412 0.293 0.207
## [11041] 0.224 0.191 0.192 0.196 0.244 0.286 0.212 0.247 0.275 0.270 0.286 0.244
## [11053] 0.178 0.187 0.191 0.190 0.243 0.199 0.206 0.190 0.251 0.221 0.246 0.261
## [11065] 0.242 0.217 0.206 0.186 0.184 0.205 0.217 0.207 0.177 0.201 0.192 0.172
## [11077] 0.253 0.190 0.328 0.164 0.366 0.325 0.298 0.269 0.190 0.256 0.232 0.242
## [11089] 0.269 0.425 0.225 0.350 0.243 0.263 0.170 0.164 0.213 0.240 0.215 0.180
## [11101] 0.208 0.266 0.236 0.214 0.228 0.199 0.221 0.258 0.164 0.166 0.176 0.177
## [11113] 0.176 0.168 0.192 0.200 0.173 0.169 0.354 0.274 0.197 0.202 0.164 0.430
## [11125] 0.184 0.187 0.283 0.328 0.320 0.185 0.326 0.186 0.267 0.200 0.219 0.238
## [11137] 0.191 0.256 0.195 0.239 0.370 0.196 0.285 0.252 0.187 0.173 0.164 0.164
## [11149] 0.211 0.182 0.202 0.377 0.335 0.186 0.175 0.245 0.260 0.237 0.202 0.221
## [11161] 0.254 0.246 0.200 0.172 0.237 0.183 0.197 0.181 0.172 0.169 0.173 0.177
## [11173] 0.171 0.167 0.167 0.173 0.172 0.164 0.164 0.199 0.186 0.201 0.162 0.165
## [11185] 0.180 0.191 0.162 0.162 0.163 0.162 0.309 0.190 0.220 0.230 0.163 0.171
## [11197] 0.197 0.305 0.249 0.279 0.275 0.171 0.340 0.279 0.268 0.352 0.427 0.649
## [11209] 0.331 0.465 0.321 0.279 0.228 0.486 0.198 0.253 0.338 0.325 0.269 0.162
## [11221] 0.162 0.163 0.163 0.162 0.162 0.162 0.166 0.181 0.187 0.217 0.206 0.204
## [11233] 0.250 0.185 0.245 0.228 0.202 0.184 0.196 0.192 0.185 0.183 0.204 0.181
## [11245] 0.163 0.163 0.163 0.163 0.165 0.168 0.164 0.166 0.165 0.163 0.163 0.168
## [11257] 0.165 0.477 0.165 0.472 0.165 0.380 0.166 0.311 0.165 0.372 0.410 0.439
## [11269] 0.406 0.468 0.440 0.229 0.219 0.201 0.209 0.165 0.243 0.165 0.163 0.165
## [11281] 0.165 0.165 0.165 0.163 0.165 0.163 0.163 0.163 0.207 0.293 0.332 0.164
## [11293] 0.175 0.358 0.165 0.167 0.162 0.163 0.162 0.164 0.192 0.196 0.190 0.187
## [11305] 0.165 0.168 0.170 0.206 0.288 0.264 0.293 0.169 0.298 0.216 0.284 0.180
## [11317] 0.185 0.174 0.173 0.171 0.164 0.162 0.237 0.314 0.238 0.179 0.171 0.183
## [11329] 0.166 0.352 0.345 0.169 0.228 0.168 0.173 0.285 0.339 0.173 0.363 0.184
## [11341] 0.220 0.267 0.173 0.350 0.169 0.189 0.173 0.173 0.166 0.171 0.167 0.188
## [11353] 0.185 0.203 0.196 0.171 0.189 0.177 0.195 0.174 0.196 0.178 0.200 0.176
## [11365] 0.185 0.180 0.170 0.183 0.195 0.185 0.195 0.190 0.166 0.166 0.163 0.164
## [11377] 0.167 0.166 0.168 0.182 0.201 0.177 0.165 0.163 0.165 0.163 0.164 0.163
## [11389] 0.165 0.166 0.166 0.164 0.163 0.164 0.163 0.165 0.165 0.163 0.166 0.165
## [11401] 0.165 0.166 0.166 0.166 0.168 0.166 0.175 0.175 0.164 0.164 0.191 0.166
## [11413] 0.167 0.168 0.165 0.165 0.165 0.166 0.166 0.167 0.166 0.171 0.170 0.166
## [11425] 0.167 0.170 0.171 0.173 0.166 0.166 0.167 0.166 0.169 0.168 0.213 0.176
## [11437] 0.169 0.169 0.209 0.199 0.164 0.164 0.165 0.164 0.164 0.165 0.164 0.164
## [11449] 0.164 0.164 0.164 0.166 0.164 0.169 0.164 0.164 0.164 0.164 0.164 0.210
## [11461] 0.301 0.484 0.222 0.189 0.174 0.212 0.222 0.167 0.201 0.213 0.186 0.303
## [11473] 0.194 0.191 0.219 0.206 0.186 0.183 0.170 0.178 0.170 0.169 0.173 0.169
## [11485] 0.167 0.171 0.226 0.286 0.275 0.333 0.326 0.210 0.498 0.356 0.172 0.165
## [11497] 0.166 0.164 0.164 0.165 0.164 0.164 0.165 0.164 0.164 0.164 0.164 0.164
## [11509] 0.164 0.164 0.164 0.164 0.164 0.178 0.167 0.171 0.175 0.168 0.167 0.167
## [11521] 0.165 0.168 0.166 0.165 0.169 0.166 0.165 0.168 0.165 0.166 0.164 0.164
## [11533] 0.169 0.164 0.164 0.164 0.165 0.165 0.164 0.180 0.179 0.194 0.206 0.191
## [11545] 0.190 0.206 0.231 0.178 0.168 0.165 0.167 0.174 0.176 0.178 0.190 0.192
## [11557] 0.180 0.175 0.191 0.193 0.206 0.184 0.233 0.212 0.190 0.174 0.179 0.187
## [11569] 0.199 0.592 0.164 0.164 0.164 0.191 0.164 0.194 0.199 0.197 0.198 0.195
## [11581] 0.194 0.227 0.183 0.175 0.184 0.167 0.173 0.171 0.166 0.188 0.168 0.202
## [11593] 0.164 0.164 0.164 0.164 0.384 0.184 0.366 0.185 0.227 0.216 0.213 0.315
## [11605] 0.180 0.179 0.187 0.170 0.168 0.169 0.167 0.167 0.197 0.265 0.231 0.222
## [11617] 0.327 0.242 0.327 0.250 0.325 0.209 0.164 0.169 0.165 0.166 0.169 0.177
## [11629] 0.176 0.199 0.345 0.358 0.164 0.170 0.177 0.170 0.176 0.172 0.164 0.164
## [11641] 0.164 0.164 0.164 0.164 0.299 0.380 0.327 0.358 0.329 0.335 0.297 0.312
## [11653] 0.164 0.298 0.262 0.213 0.203 0.195 0.223 0.233 0.217 0.254 0.203 0.207
## [11665] 0.330 0.346 0.293 0.242 0.352 0.342 0.296 0.164 0.164 0.164 0.164 0.193
## [11677] 0.191 0.223 0.204 0.195 0.199 0.168 0.501 0.194 0.198 0.177 0.197 0.409
## [11689] 0.245 0.439 0.458 0.181 0.243 0.220 0.314 0.300 0.351 0.272 0.231 0.255
## [11701] 0.347 0.165 0.165 0.166 0.166 0.179 0.195 0.180 0.169 0.165 0.173 0.166
## [11713] 0.168 0.166 0.166 0.165 0.167 0.168 0.167 0.166 0.165 0.170 0.170 0.169
## [11725] 0.166 0.168 0.166 0.164 0.185 0.174 0.167 0.175 0.164 0.174 0.165 0.164
## [11737] 0.168 0.164 0.167 0.165 0.171 0.168 0.200 0.269 0.177 0.170 0.173 0.164
## [11749] 0.175 0.164 0.486 0.477 0.165 0.164 0.244 0.174 0.162 0.162 0.182 0.227
## [11761] 0.192 0.199 0.202 0.162 0.165 0.173 0.188 0.224 0.246 0.165 0.165 0.188
## [11773] 0.165 0.317 0.307 0.244 0.282 0.187 0.165 0.164 0.182 0.181 0.185 0.225
## [11785] 0.179 0.170 0.178 0.176 0.175 0.166 0.171 0.171 0.175 0.188 0.169 0.169
## [11797] 0.168 0.167 0.170 0.190 0.226 0.211 0.177 0.164 0.164 0.164 0.164 0.164
## [11809] 0.164 0.163 0.164 0.168 0.164 0.164 0.164 0.173 0.164 0.164 0.164 0.164
## [11821] 0.184 0.246 0.320 0.311 0.357 0.249 0.573 0.258 0.275 0.235 0.186 0.176
## [11833] 0.178 0.198 0.172 0.177 0.179 0.168 0.181 0.171 0.170 0.179 0.183 0.218
## [11845] 0.252 0.185 0.164 0.164 0.192 0.164 0.167 0.173 0.187 0.170 0.182 0.178
## [11857] 0.182 0.268 0.177 0.218 0.216 0.173 0.258 0.164 0.165 0.166 0.166 0.164
## [11869] 0.165 0.167 0.166 0.167 0.165 0.166 0.165 0.165 0.167 0.167 0.167 0.175
## [11881] 0.186 0.265 0.189 0.165 0.165 0.164 0.165 0.170 0.164 0.165 0.178 0.202
## [11893] 0.197 0.204 0.218 0.197 0.236 0.233 0.186 0.178 0.195 0.176 0.227 0.223
## [11905] 0.269 0.240 0.214 0.196 0.191 0.191 0.204 0.219 0.204 0.195 0.187 0.190
## [11917] 0.189 0.243 0.168 0.177 0.201 0.182 0.182 0.206 0.180 0.212 0.236 0.272
## [11929] 0.205 0.254 0.210 0.222 0.210 0.184 0.198 0.228 0.221 0.185 0.216 0.492
## [11941] 0.196 0.252 0.268 0.706 0.607 0.201 0.172 0.210 0.206 0.208 0.214 0.203
## [11953] 0.169 0.205 0.289 0.326 0.319 0.188 0.227 0.188 0.210 0.189 0.187 0.164
## [11965] 0.164 0.170 0.169 0.170 0.171 0.169 0.165 0.173 0.171 0.168 0.168 0.166
## [11977] 0.165 0.168 0.165 0.168 0.165 0.167 0.168 0.171 0.171 0.185 0.183 0.176
## [11989] 0.173 0.175 0.168 0.170 0.214 0.189 0.185 0.175 0.211 0.195 0.217 0.236
## [12001] 0.225 0.230 0.168 0.166 0.168 0.170 0.172 0.185 0.181 0.179 0.189 0.191
## [12013] 0.183 0.186 0.181 0.188 0.188 0.274 0.333 0.294 0.256 0.182 0.187 0.201
## [12025] 0.189 0.199 0.178 0.179 0.492 0.263 0.164 0.168 0.165 0.239 0.230 0.246
## [12037] 0.163 0.166 0.166 0.176 0.164 0.165 0.169 0.164 0.166 0.177 0.180 0.536
## [12049] 0.426 0.164 0.172 0.177 0.178 0.163 0.163 0.172 0.341 0.207 0.167 0.167
## [12061] 0.167 0.166 0.165 0.164 0.167 0.167 0.166 0.166 0.165 0.165 0.166 0.165
## [12073] 0.165 0.165 0.165 0.165 0.165 0.166 0.166 0.171 0.168 0.170 0.166 0.168
## [12085] 0.170 0.227 0.263 0.216 0.276 0.286 0.277 0.240 0.241 0.261 0.181 0.259
## [12097] 0.223 0.179 0.283 0.283 0.234 0.208 0.193 0.168 0.165 0.165 0.178 0.211
## [12109] 0.182 0.189 0.185 0.192 0.174 0.321 0.164 0.250 0.269 0.360 0.299 0.222
## [12121] 0.330 0.238 0.330 0.274 0.238 0.320 0.313 0.315 0.407 0.331 0.247 0.316
## [12133] 0.237 0.180 0.178 0.199 0.164 0.164 0.165 0.164 0.164 0.164 0.164 0.179
## [12145] 0.164 0.162 0.165 0.164 0.171 0.176 0.216 0.264 0.253 0.181 0.198 0.182
## [12157] 0.178 0.207 0.214 0.240 0.165 0.165 0.164 0.164 0.164 0.163 0.163 0.165
## [12169] 0.165 0.164 0.164 0.164 0.165 0.166 0.165 0.165 0.165 0.165 0.164 0.165
## [12181] 0.165 0.165 0.166 0.165 0.164 0.165 0.166 0.165 0.164 0.165 0.164 0.165
## [12193] 0.164 0.164 0.165 0.165 0.165 0.170 0.165 0.165 0.167 0.166 0.167 0.170
## [12205] 0.166 0.167 0.165 0.166 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165
## [12217] 0.164 0.166 0.166 0.198 0.177 0.193 0.169 0.178 0.164 0.169 0.165 0.166
## [12229] 0.165 0.168 0.165 0.175 0.170 0.173 0.180 0.179 0.184 0.171 0.165 0.167
## [12241] 0.169 0.166 0.164 0.198 0.171 0.180 0.179 0.169 0.217 0.253 0.263 0.187
## [12253] 0.186 0.194 0.173 0.170 0.171 0.175 0.169 0.171 0.177 0.174 0.172 0.175
## [12265] 0.199 0.197 0.183 0.196 0.205 0.188 0.195 0.171 0.163 0.163 0.165 0.167
## [12277] 0.165 0.165 0.164 0.164 0.164 0.165 0.165 0.165 0.164 0.168 0.165 0.165
## [12289] 0.165 0.165 0.164 0.164 0.164 0.166 0.166 0.165 0.166 0.166 0.166 0.165
## [12301] 0.165 0.167 0.165 0.165 0.164 0.405 0.242 0.201 0.254 0.287 0.773 0.373
## [12313] 0.328 0.504 0.182 0.173 0.169 0.168 0.165 0.165 0.315 0.170 0.215 0.165
## [12325] 0.183 0.165 0.167 0.165 0.238 0.318 0.181 0.214 0.251 0.190 0.238 0.347
## [12337] 0.312 0.230 0.182 0.222 0.389 0.372 0.334 0.192 0.281 0.297 0.333 0.249
## [12349] 0.164 0.180 0.185 0.215 0.278 0.232 0.234 0.164 0.164 0.165 0.173 0.175
## [12361] 0.209 0.260 0.164 0.167 0.164 0.172 0.170 0.204 0.202 0.189 0.200 0.168
## [12373] 0.307 0.521 0.209 0.242 0.250 0.260 0.257 0.315 0.164 0.164 0.165 0.166
## [12385] 0.162 0.169 0.173 0.175 0.167 0.166 0.172 0.164 0.163 0.165 0.163 0.167
## [12397] 0.166 0.163 0.165 0.163 0.176 0.196 0.193 0.186 0.172 0.171 0.170 0.175
## [12409] 0.189 0.271 0.663 0.185 0.167 0.166 0.167 0.214 0.289 0.171 0.212 0.184
## [12421] 0.169 0.166 0.182 0.183 0.172 0.177 0.180 0.181 0.170 0.181 0.165 0.163
## [12433] 0.164 0.164 0.164 0.164 0.163 0.163 0.164 0.164 0.164 0.165 0.166 0.164
## [12445] 0.164 0.164 0.164 0.163 0.164 0.164 0.163 0.163 0.163 0.163 0.163 0.163
## [12457] 0.163 0.163 0.196 0.164 0.164 0.306 0.241 0.382 0.163 0.239 0.163 0.163
## [12469] 0.168 0.176 0.229 0.233 0.280 0.291 0.220 0.297 0.163 0.164 0.163 0.163
## [12481] 0.163 0.165 0.165 0.164 0.163 0.163 0.163 0.164 0.165 0.164 0.164 0.164
## [12493] 0.165 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.165 0.165 0.164 0.175
## [12505] 0.593 1.032 0.737 0.262 0.164 0.164 0.164 0.164 0.236 0.211 0.238 0.164
## [12517] 0.185 0.221 0.263 0.190 0.189 0.174 0.168 0.303 0.350 0.202 0.162 0.162
## [12529] 0.187 0.162 0.163 0.190 0.260 0.296 0.224 0.184 0.243 0.202 0.180 0.177
## [12541] 0.241 0.181 0.171 0.176 0.174 0.178 0.202 0.178 0.179 0.177 0.304 0.179
## [12553] 0.175 0.176 0.178 0.182 0.172 0.170 0.170 0.176 0.176 0.170 0.172 0.173
## [12565] 0.169 0.173 0.176 0.177 0.172 0.163 0.247 0.196 0.213 0.238 0.226 0.211
## [12577] 0.219 0.165 0.165 0.165 0.165 0.277 0.162 0.162 0.163 0.177 0.165 0.164
## [12589] 0.164 0.164 0.164 0.168 0.190 0.183 0.183 0.182 0.176 0.185 0.172 0.172
## [12601] 0.166 0.184 0.172 0.180 0.176 0.187 0.177 0.176 0.171 0.173 0.168 0.166
## [12613] 0.167 0.168 0.168 0.169 0.173 0.173 0.171 0.175 0.185 0.165 0.169 0.169
## [12625] 0.170 0.174 0.169 0.165 0.171 0.165 0.231 0.177 0.173 0.173 0.172 0.172
## [12637] 0.173 0.171 0.171 0.170 0.172 0.174 0.178 0.173 0.175 0.183 0.207 0.170
## [12649] 0.172 0.170 0.171 0.171 0.294 0.179 0.278 0.186 0.182 0.303 0.172 0.226
## [12661] 0.181 0.282 0.277 0.215 0.176 0.186 0.181 0.186 0.191 0.193 0.184 0.175
## [12673] 0.197 0.166 0.169 0.166 0.170 0.171 0.169 0.170 0.167 0.165 0.167 0.166
## [12685] 0.179 0.172 0.169 0.174 0.182 0.175 0.172 0.167 0.232 0.168 0.232 0.167
## [12697] 0.188 0.166 0.191 0.174 0.199 0.175 0.191 0.175 0.183 0.173 0.181 0.170
## [12709] 0.177 0.173 0.178 0.177 0.174 0.179 0.178 0.188 0.175 0.174 0.171 0.168
## [12721] 0.169 0.203 0.164 0.165 0.164 0.164 0.175 0.172 0.164 0.164 0.183 0.170
## [12733] 0.164 0.170 0.205 0.168 0.170 0.173 0.176 0.175 0.177 0.173 0.169 0.168
## [12745] 0.169 0.171 0.169 0.170 0.171 0.171 0.168 0.166 0.167 0.197 0.164 0.165
## [12757] 0.164 0.164 0.170 0.165 0.176 0.183 0.225 0.296 0.179 0.246 0.179 0.218
## [12769] 0.220 0.192 0.176 0.179 0.172 0.184 0.175 0.170 0.170 0.172 0.172 0.173
## [12781] 0.169 0.170 0.169 0.169 0.170 0.168 0.167 0.172 0.172 0.189 0.184 0.184
## [12793] 0.174 0.178 0.181 0.176 0.171 0.177 0.177 0.174 0.178 0.177 0.187 0.180
## [12805] 0.184 0.180 0.168 0.168 0.215 0.165 0.165 0.167 0.165 0.174 0.177 0.173
## [12817] 0.182 0.185 0.180 0.178 0.186 0.187 0.176 0.175 0.185 0.183 0.184 0.182
## [12829] 0.186 0.188 0.187 0.186 0.180 0.165 0.168 0.196 0.164 0.165 0.166 0.169
## [12841] 0.175 0.174 0.172 0.172 0.176 0.173 0.178 0.179 0.174 0.289 0.177 0.223
## [12853] 0.271 0.247 0.278 0.175 0.248 0.167 0.201 0.313 0.178 0.211 0.170 0.173
## [12865] 0.173 0.170 0.169 0.173 0.171 0.171 0.171 0.187 0.177 0.172 0.171 0.177
## [12877] 0.176 0.182 0.170 0.176 0.172 0.173 0.169 0.165 0.166 0.174 0.172 0.168
## [12889] 0.176 0.172 0.172 0.168 0.173 0.165 0.172 0.171 0.233 0.192 0.184 0.293
## [12901] 0.215 0.282 0.278 0.337 0.255 0.321 0.183 0.168 0.169 0.165 0.181 0.172
## [12913] 0.180 0.191 0.183 0.177 0.176 0.306 0.171 0.176 0.188 0.172 0.172 0.174
## [12925] 0.198 0.212 0.233 0.180 0.203 0.212 0.202 0.182 0.181 0.177 0.179 0.185
## [12937] 0.204 0.189 0.190 0.185 0.197 0.184 0.177 0.187 0.176 0.186 0.182 0.177
## [12949] 0.180 0.177 0.168 0.165 0.163 0.165 0.165 0.165 0.165 0.177 0.168 0.169
## [12961] 0.170 0.171 0.170 0.169 0.168 0.168 0.167 0.172 0.170 0.171 0.171 0.173
## [12973] 0.167 0.168 0.166 0.165 0.165 0.166 0.165 0.165 0.164 0.164 0.165 0.165
## [12985] 0.179 0.185 0.190 0.196 0.186 0.188 0.190 0.172 0.175 0.174 0.181 0.176
## [12997] 0.176 0.177 0.179 0.181 0.189 0.180 0.169 0.170 0.173 0.172 0.172 0.167
## [13009] 0.177 0.178 0.180 0.176 0.181 0.211 0.177 0.218 0.200 0.194 0.215 0.208
## [13021] 0.195 0.194 0.189 0.192 0.192 0.187 0.195 0.179 0.173 0.179 0.186 0.175
## [13033] 0.185 0.180 0.188 0.178 0.189 0.198 0.180 0.178 0.175 0.168 0.171 0.172
## [13045] 0.169 0.169 0.172 0.191 0.186 0.183 0.182 0.184 0.177 0.190 0.174 0.174
## [13057] 0.183 0.187 0.178 0.175 0.182 0.186 0.189 0.186 0.181 0.168 0.175 0.167
## [13069] 0.171 0.169 0.174 0.171 0.174 0.171 0.233 0.181 0.193 0.179 0.179 0.185
## [13081] 0.172 0.194 0.204 0.194 0.204 0.205 0.194 0.195 0.190 0.187 0.185 0.178
## [13093] 0.186 0.186 0.180 0.201 0.193 0.188 0.192 0.178 0.184 0.175 0.185 0.178
## [13105] 0.167 0.176 0.183 0.187 0.184 0.176 0.174 0.176 0.180 0.174 0.193 0.181
## [13117] 0.180 0.187 0.184 0.181 0.176 0.177 0.177 0.177 0.174 0.176 0.179 0.177
## [13129] 0.178 0.179 0.179 0.175 0.186 0.194 0.180 0.190 0.182 0.178 0.188 0.179
## [13141] 0.165 0.166 0.168 0.167 0.168 0.173 0.170 0.172 0.175 0.173 0.171 0.170
## [13153] 0.180 0.171 0.169 0.170 0.169 0.170 0.170 0.170 0.171 0.173 0.169 0.167
## [13165] 0.167 0.168 0.169 0.173 0.168 0.168 0.165 0.168 0.168 0.165 0.165 0.167
## [13177] 0.168 0.169 0.174 0.178 0.175 0.174 0.176 0.171 0.172 0.173 0.171 0.169
## [13189] 0.170 0.168 0.169 0.169 0.170 0.169 0.172 0.172 0.168 0.165 0.166 0.166
## [13201] 0.167 0.166 0.167 0.166 0.169 0.167 0.213 0.169 0.169 0.171 0.176 0.179
## [13213] 0.170 0.210 0.178 0.178 0.186 0.178 0.177 0.173 0.172 0.172 0.169 0.171
## [13225] 0.183 0.177 0.175 0.177 0.185 0.186 0.170 0.182 0.177 0.169 0.181 0.178
## [13237] 0.171 0.171 0.173 0.169 0.170 0.170 0.173 0.175 0.172 0.178 0.176 0.175
## [13249] 0.176 0.172 0.174 0.172 0.172 0.174 0.174 0.173 0.171 0.173 0.174 0.168
## [13261] 0.167 0.172 0.176 0.178 0.170 0.172 0.178 0.173 0.178 0.169 0.174 0.174
## [13273] 0.189 0.178 0.180 0.174 0.181 0.187 0.170 0.231 0.202 0.202 0.195 0.187
## [13285] 0.180 0.181 0.176 0.176 0.170 0.169 0.171 0.174 0.178 0.180 0.182 0.173
## [13297] 0.180 0.186 0.186 0.174 0.177 0.176 0.171 0.173 0.178 0.171 0.171 0.171
## [13309] 0.168 0.172 0.181 0.199 0.186 0.178 0.184 0.178 0.184 0.185 0.174 0.169
## [13321] 0.169 0.173 0.167 0.169 0.171 0.170 0.171 0.169 0.166 0.168 0.171 0.168
## [13333] 0.170 0.173 0.178 0.165 0.167 0.168 0.166 0.165 0.167 0.167 0.167 0.176
## [13345] 0.177 0.175 0.173 0.173 0.175 0.177 0.176 0.171 0.178 0.176 0.172 0.173
## [13357] 0.179 0.175 0.171 0.173 0.178 0.171 0.166 0.166 0.166 0.168 0.165 0.168
## [13369] 0.165 0.167 0.166 0.192 0.199 0.189 0.208 0.199 0.206 0.198 0.184 0.185
## [13381] 0.186 0.183 0.177 0.180 0.186 0.183 0.187 0.188 0.171 0.166 0.176 0.175
## [13393] 0.170 0.166 0.164 0.165 0.165 0.167 0.166 0.168 0.171 0.179 0.176 0.183
## [13405] 0.177 0.176 0.182 0.174 0.173 0.168 0.170 0.168 0.167 0.167 0.168 0.171
## [13417] 0.169 0.169 0.166 0.168 0.167 0.167 0.166 0.167 0.166 0.165 0.166 0.166
## [13429] 0.170 0.197 0.168 0.175 0.180 0.173 0.171 0.173 0.168 0.168 0.169 0.168
## [13441] 0.175 0.170 0.169 0.169 0.176 0.182 0.174 0.170 0.174 0.181 0.175 0.186
## [13453] 0.176 0.186 0.174 0.174 0.179 0.177 0.180 0.180 0.177 0.174 0.174 0.175
## [13465] 0.175 0.176 0.184 0.171 0.175 0.171 0.175 0.172 0.173 0.175 0.172 0.172
## [13477] 0.174 0.171 0.167 0.172 0.173 0.178 0.179 0.182 0.176 0.184 0.186 0.169
## [13489] 0.206 0.204 0.196 0.204 0.203 0.195 0.192 0.182 0.188 0.187 0.192 0.189
## [13501] 0.198 0.189 0.196 0.201 0.190 0.186 0.188 0.186 0.181 0.186 0.181 0.177
## [13513] 0.178 0.180 0.268 0.174 0.180 0.177 0.188 0.180 0.175 0.200 0.191 0.191
## [13525] 0.202 0.205 0.187 0.179 0.175 0.178 0.172 0.177 0.179 0.182 0.179 0.186
## [13537] 0.194 0.195 0.185 0.187 0.183 0.180 0.179 0.177 0.187 0.186 0.183 0.193
## [13549] 0.189 0.196 0.204 0.197 0.185 0.180 0.208 0.203 0.190 0.199 0.211 0.192
## [13561] 0.195 0.178 0.184 0.177 0.183 0.184 0.185 0.187 0.186 0.184 0.186 0.177
## [13573] 0.198 0.180 0.186 0.177 0.180 0.184 0.197 0.181 0.250 0.180 0.194 0.180
## [13585] 0.188 0.178 0.169 0.201 0.179 0.196 0.188 0.186 0.189 0.176 0.178 0.179
## [13597] 0.180 0.181 0.185 0.181 0.186 0.185 0.189 0.185 0.179 0.182 0.188 0.174
## [13609] 0.184 0.176 0.175 0.187 0.185 0.220 0.191 0.220 0.222 0.208 0.225 0.172
## [13621] 0.200 0.174 0.178 0.178 0.208 0.205 0.212 0.225 0.200 0.226 0.226 0.227
## [13633] 0.228 0.224 0.220 0.211 0.190 0.189 0.201 0.207 0.217 0.198 0.203 0.174
## [13645] 0.206 0.204 0.177 0.192 0.190 0.194 0.187 0.203 0.185 0.196 0.187 0.187
## [13657] 0.186 0.189 0.187 0.177 0.178 0.179 0.182 0.183 0.181 0.180 0.180 0.174
## [13669] 0.176 0.181 0.181 0.185 0.179 0.189 0.184 0.190 0.184 0.189 0.183 0.166
## [13681] 0.181 0.165 0.171 0.180 0.197 0.192 0.187 0.193 0.192 0.190 0.182 0.173
## [13693] 0.173 0.185 0.176 0.177 0.181 0.178 0.180 0.181 0.189 0.176 0.173 0.176
## [13705] 0.172 0.171 0.167 0.169 0.162 0.172 0.172 0.466 0.430 0.740 1.139 0.969
## [13717] 0.844 0.886 0.556 0.854 0.610 0.604 0.765 0.594 0.569 0.884 0.932 0.935
## [13729] 1.141 0.929 0.841 0.409 0.743 0.858 0.383 0.482 0.263 0.502 1.032 0.165
## [13741] 0.188 0.334 0.165 0.229 0.165 0.264 0.299 0.282 0.546 0.511 0.470 0.184
## [13753] 0.163 0.218 0.378 0.367 0.194 0.415 0.325 0.394 0.291 0.427 0.530 0.499
## [13765] 0.323 0.165 0.435 0.234 0.191 0.176 0.178 0.182 0.180 0.178 0.171 0.175
## [13777] 0.174 0.170 0.179 0.172 0.171 0.189 0.229 0.200 0.185 0.198 0.165 0.164
## [13789] 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165
## [13801] 0.165 0.165 0.165 0.165 0.165 0.165 0.164 0.164 0.164 0.165 0.164 0.164
## [13813] 0.164 0.164 0.164 0.162 0.162 0.164 0.165 0.173 0.165 0.165 0.164 0.243
## [13825] 0.183 0.184 0.332 0.164 0.164 0.246 0.177 0.258 0.163 0.237 0.164 0.226
## [13837] 0.175 0.467 0.167 0.386 0.164 0.424 0.164 0.389 0.164 0.432 0.164 0.242
## [13849] 0.164 0.164 0.167 0.258 0.222 0.379 0.196 0.289 0.264 0.332 0.314 0.302
## [13861] 0.275 0.278 0.176 0.175 0.181 0.165 0.168 0.169 0.168 0.215 0.175 0.206
## [13873] 0.185 0.432 0.249 0.575 0.471 0.215 0.207 0.202 0.164 0.316 0.323 0.314
## [13885] 0.339 0.198 0.302 0.249 0.228 0.195 0.195 0.240 0.214 0.283 0.347 0.252
## [13897] 0.333 0.258 0.201 0.184 0.177 0.169 0.208 0.200 0.184 0.200 0.190 0.184
## [13909] 0.187 0.166 0.165 0.168 0.165 0.168 0.169 0.170 0.166 0.165 0.166 0.164
## [13921] 0.165 0.166 0.166 0.165 0.231 0.164 0.164 0.164 0.164 0.164 0.165 0.165
## [13933] 0.165 0.165 0.165 0.164 0.164 0.227 0.164 0.165 0.163 0.169 0.164 0.167
## [13945] 0.164 0.163 0.164 0.164 0.164 0.163 0.164 0.164 0.164 0.164 0.164 0.163
## [13957] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.211 0.164
## [13969] 0.165 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.163 0.163 0.163 0.163
## [13981] 0.163 0.163 0.162 0.162 0.163 0.164 0.164 0.164 0.164 0.163 0.165 0.164
## [13993] 0.165 0.165 0.164 0.166 0.163 0.162 0.164 0.164 0.165 0.164 0.164 0.219
## [14005] 0.163 0.163 0.164 0.164 0.164 0.164 0.164 0.164 0.411 0.168 0.164 0.164
## [14017] 0.165 0.348 0.163 0.163 0.163 0.163 0.163 0.163 0.544 0.181 0.267 0.168
## [14029] 0.453 0.164 0.164 0.163 0.165 0.164 0.165 0.165 0.164 0.163 0.163 0.162
## [14041] 0.164 0.164 0.165 0.164 0.164 0.163 0.163 0.165 0.164 0.164 0.164 0.164
## [14053] 0.166 0.168 0.164 0.164 0.168 0.167 0.163 0.165 0.163 0.166 0.163 0.163
## [14065] 0.162 0.162 0.163 0.164 0.164 0.164 0.164 0.163 0.165 0.164 0.165 0.165
## [14077] 0.164 0.163 0.163 0.162 0.164 0.164 0.165 0.164 0.164 0.324 0.175 0.167
## [14089] 0.164 0.164 0.169 0.164 0.193 0.306 0.254 0.224 0.211 0.197 0.423 0.291
## [14101] 0.163 0.255 0.163 0.332 0.343 0.225 0.228 0.203 0.164 0.164 0.164 0.164
## [14113] 0.168 0.165 0.165 0.164 0.165 0.165 0.164 0.165 0.167 0.166 0.170 0.166
## [14125] 0.165 0.167 0.164 0.172 0.164 0.167 0.165 0.170 0.229 0.164 0.164 0.164
## [14137] 0.164 0.164 0.164 0.164 0.164 0.165 0.165 0.165 0.165 0.165 0.165 0.164
## [14149] 0.165 0.164 0.164 0.163 0.164 0.164 0.165 0.209 0.164 0.163 0.165 0.164
## [14161] 0.168 0.196 0.210 0.224 0.227 0.215 0.174 0.207 0.194 0.219 0.194 0.218
## [14173] 0.223 0.218 0.196 0.195 0.164 0.164 0.164 0.164 0.164 0.165 0.165 0.165
## [14185] 0.165 0.165 0.164 0.220 0.211 0.210 0.193 0.217 0.191 0.332 0.383 0.177
## [14197] 0.182 0.168 0.644 0.195 0.234 0.340 0.168 0.170 0.166 0.168 0.169 0.167
## [14209] 0.170 0.165 0.169 0.164 0.171 0.189 0.171 0.164 0.172 0.182 0.181 0.182
## [14221] 0.167 0.179 0.175 0.167 0.196 0.164 0.168 0.167 0.164 0.164 0.170 0.174
## [14233] 0.173 0.171 0.177 0.168 0.174 0.171 0.182 0.169 0.169 0.167 0.164 0.164
## [14245] 0.167 0.167 0.167 0.180 0.168 0.174 0.169 0.164 0.165 0.164 0.166 0.164
## [14257] 0.173 0.376 0.173 0.283 0.172 0.260 0.165 0.170 0.174 0.166 0.163 0.165
## [14269] 0.170 0.302 0.165 0.164 0.164 0.177 0.163 0.164 0.163 0.164 0.164 0.164
## [14281] 0.164 0.162 0.224 0.187 0.249 0.315 0.343 0.409 0.164 0.165 0.162 0.165
## [14293] 0.165 0.165 0.165 0.165 0.165 0.164 0.164 0.163 0.164 0.164 0.165 0.164
## [14305] 0.163 0.164 0.168 0.170 0.172 0.173 0.171 0.173 0.179 0.177 0.174 0.175
## [14317] 0.178 0.171 0.173 0.175 0.182 0.187 0.164 0.164 0.164 0.164 0.164 0.165
## [14329] 0.165 0.165 0.165 0.165 0.164 0.186 0.173 0.173 0.176 0.170 0.222 0.300
## [14341] 0.242 0.275 0.286 0.284 0.250 0.246 0.267 0.238 0.284 0.239 0.215 0.163
## [14353] 0.233 0.163 0.163 0.172 0.164 0.164 0.164 0.164 0.164 0.164 0.165 0.164
## [14365] 0.165 0.165 0.165 0.164 0.163 0.165 0.164 0.164 0.162 0.165 0.162 0.317
## [14377] 0.164 0.164 0.165 0.164 0.164 0.165 0.163 0.164 0.165 0.163 0.165 0.163
## [14389] 0.165 0.164 0.164 0.162 0.175 0.165 0.177 0.180 0.164 0.197 0.248 0.164
## [14401] 0.164 0.165 0.162 0.165 0.165 0.165 0.165 0.164 0.165 0.164 0.164 0.163
## [14413] 0.164 0.164 0.165 0.164 0.163 0.164 0.224 0.162 0.165 0.164 0.164 0.165
## [14425] 0.163 1.002 1.644 0.609 1.024 0.366 0.281 0.166 0.226 0.233 0.191 0.168
## [14437] 0.163 0.167 0.166 0.167 0.164 0.165 0.165 0.165 0.164 0.164 0.165 0.164
## [14449] 0.164 0.170 0.165 0.201 0.165 0.172 0.166 0.168 0.175 0.163 0.165 0.165
## [14461] 0.165 0.830 1.231 0.465 0.462 0.385 0.407 0.713 0.524 0.486 0.430 1.243
## [14473] 0.934 0.620 0.467 0.293 0.529 0.379 0.707 0.269 0.728 1.108 0.730 1.815
## [14485] 0.214 0.362 0.253 0.588 0.290 0.200 0.168 0.165 0.165 0.164 0.525 1.322
## [14497] 0.271 0.210 0.170 0.373 0.258 0.239 0.520 0.444 0.177 0.172 0.265 0.578
## [14509] 0.164 0.303 0.173 0.165 0.164 0.165 0.165 0.189 0.165 0.316 0.172 0.165
## [14521] 0.165 0.165 0.165 0.191 1.563 1.095 0.164 0.194 0.174 0.165 0.164 0.164
## [14533] 0.164 0.840 0.611 0.164 0.804 0.202 0.195 0.701 0.805 0.219 0.977 0.580
## [14545] 0.452 0.357 0.386 0.165 0.227 0.377 1.584 1.109 0.531 0.722 0.181 0.216
## [14557] 0.191 0.178 0.164 1.350 0.842 0.586 1.062 0.393 0.382 0.564 0.867 0.243
## [14569] 0.861 0.562 0.288 0.745 0.321 0.342 0.238 1.095 0.901 0.456 0.465 0.696
## [14581] 0.641 0.882 0.919 0.854 0.243 0.569 0.299 0.346 0.310 0.533 0.914 1.505
## [14593] 0.782 0.731 0.890 0.904 0.644 0.702 0.430 0.666 0.328 0.429 0.168 0.185
## [14605] 0.236 0.440 0.231 0.170 0.580 0.470 1.003 0.865 0.395 0.891 0.754 0.497
## [14617] 0.604 0.300 0.347 0.165 0.165 0.164 0.164 0.164 0.164 0.164 0.163 0.216
## [14629] 0.166 0.180 0.172 0.193 0.202 0.165 0.249 0.167 0.164 0.177 0.164 0.164
## [14641] 0.164 0.173 0.164 0.208 0.170 0.170 0.173 0.167 0.174 0.173 0.251 0.243
## [14653] 0.230 0.306 0.226 0.164 0.167 0.269 0.164 0.165 0.164 0.164 0.164 0.164
## [14665] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.183 0.490 0.165 0.347 0.165
## [14677] 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.163 0.164
## [14689] 0.170 0.308 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.164 0.164 0.164
## [14701] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.165 0.164 0.164 0.164
## [14713] 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165
## [14725] 0.164 0.164 0.164 0.168 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.166
## [14737] 0.164 0.164 0.164 0.164 0.179 0.182 0.239 0.167 0.164 0.166 0.164 0.176
## [14749] 0.165 0.173 0.165 0.164 0.173 0.164 0.173 0.169 0.174 0.164 0.174 0.164
## [14761] 0.175 0.164 0.174 0.172 0.172 0.175 0.165 0.165 0.165 0.165 0.165 0.165
## [14773] 0.165 0.165 0.165 0.164 0.165 0.164 0.184 0.164 0.295 0.164 0.247 0.173
## [14785] 0.280 0.164 0.212 0.181 0.287 0.195 0.222 0.282 0.270 0.286 0.200 0.263
## [14797] 0.293 0.282 0.251 0.290 0.175 0.274 0.197 0.308 0.174 0.277 0.166 0.289
## [14809] 0.164 0.251 0.164 0.253 0.164 0.280 0.167 0.200 0.166 0.205 0.166 0.219
## [14821] 0.166 0.200 0.166 0.323 0.172 0.277 0.220 0.762 0.499 0.326 0.390 0.200
## [14833] 0.186 0.164 0.164 0.205 0.164 0.272 0.164 0.247 0.218 0.276 0.223 0.232
## [14845] 0.248 0.163 0.231 0.274 0.273 0.215 0.164 0.300 0.171 0.310 0.169 0.237
## [14857] 0.164 0.222 0.164 0.164 0.164 0.164 0.164 0.164 0.166 0.228 0.294 0.291
## [14869] 0.264 0.214 0.215 0.165 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [14881] 0.165 0.164 0.163 0.381 0.300 0.372 0.185 0.163 0.164 0.164 0.164 0.164
## [14893] 0.164 0.164 0.168 0.164 0.164 0.164 0.164 0.174 0.739 0.251 0.260 0.178
## [14905] 0.164 0.164 0.164 0.164 0.165 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [14917] 0.180 0.424 0.337 0.409 0.499 0.531 0.225 0.175 0.166 0.196 0.323 0.164
## [14929] 0.165 0.164 0.164 0.165 0.164 0.165 0.164 0.164 0.165 0.164 0.164 0.163
## [14941] 0.164 0.163 0.164 0.164 0.164 0.164 0.164 0.164 0.163 0.165 0.164 0.164
## [14953] 0.165 0.164 0.164 0.165 0.164 0.165 0.164 0.165 0.164 0.165 0.164 0.165
## [14965] 0.164 0.164 0.165 0.164 0.163 0.165 0.164 0.163 0.165 0.164 0.164 0.165
## [14977] 0.164 0.165 0.164 0.164 0.165 0.164 0.165 0.164 0.164 0.165 0.164 0.164
## [14989] 0.165 0.164 0.164 0.179 0.164 0.164 0.165 0.164 0.164 0.164 0.165 0.164
## [15001] 0.164 0.165 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.163
## [15013] 0.164 0.164 0.163 0.165 0.163 0.164 0.163 0.163 0.164 0.164 0.164 0.164
## [15025] 0.164 0.163 0.164 0.164 0.163 0.163 0.164 0.163 0.164 0.165 0.162 0.164
## [15037] 0.162 0.164 0.162 0.164 0.229 0.164 0.211 0.164 0.164 0.164 0.162 0.164
## [15049] 0.164 0.164 0.165 0.164 0.163 0.165 0.165 0.164 0.164 0.164 0.281 0.164
## [15061] 0.164 0.165 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.204
## [15073] 0.179 0.164 0.178 0.164 0.164 0.577 0.163 0.737 0.163 0.164 0.164 0.163
## [15085] 0.164 0.165 0.165 0.164 0.164 0.165 0.164 0.165 0.222 0.164 0.165 0.222
## [15097] 0.164 0.164 0.164 0.164 0.164 0.163 0.164 0.164 0.164 0.164 0.177 0.164
## [15109] 0.164 0.164 0.164 0.164 0.164 0.163 0.164 0.164 0.163 0.164 0.164 0.163
## [15121] 0.163 0.164 0.163 0.163 0.164 0.164 0.164 0.162 0.165 0.714 0.203 0.683
## [15133] 0.214 0.194 0.199 0.214 0.201 0.230 0.193 0.173 0.166 0.174 0.186 0.209
## [15145] 0.201 0.200 0.185 0.203 0.199 0.192 0.227 0.165 0.165 0.165 0.165 0.165
## [15157] 0.165 0.165 0.165 0.291 0.168 0.174 0.173 0.204 0.211 0.187 0.164 0.197
## [15169] 0.164 0.188 0.163 0.210 0.165 0.207 0.165 0.198 0.163 0.200 0.163 0.213
## [15181] 0.163 0.194 0.164 0.219 0.164 0.220 0.164 0.196 0.164 0.226 0.164 0.194
## [15193] 0.164 0.183 0.164 0.195 0.164 0.205 0.164 0.204 0.163 0.229 0.205 0.177
## [15205] 0.216 0.227 0.223 0.228 0.326 0.364 0.317 0.370 0.165 0.164 0.164 0.163
## [15217] 0.165 0.165 0.163 0.164 0.164 0.164 0.164 0.220 0.200 0.226 0.214 0.203
## [15229] 0.180 0.181 0.220 0.187 0.165 0.200 0.189 0.183 0.173 0.211 0.183 0.196
## [15241] 0.189 0.207 0.205 0.201 0.219 0.185 0.205 0.173 0.189 0.203 0.166 0.175
## [15253] 0.208 0.178 0.170 0.201 0.218 0.172 0.167 0.201 0.214 0.213 0.168 0.184
## [15265] 0.210 0.166 0.165 0.199 0.164 0.164 0.162 0.182 0.164 0.165 0.200 0.173
## [15277] 0.175 0.165 0.200 0.165 0.165 0.167 0.162 0.165 0.165 0.165 0.162 0.165
## [15289] 0.165 0.165 0.162 0.165 0.165 0.167 0.162 0.165 0.165 0.169 0.167 0.165
## [15301] 0.172 0.165 0.169 0.165 0.225 0.164 0.165 0.169 0.164 0.165 0.165 0.163
## [15313] 0.183 0.166 0.165 0.165 0.165 0.177 0.198 0.168 0.203 0.173 0.222 0.169
## [15325] 0.170 0.169 0.164 0.171 0.165 0.165 0.171 0.177 0.164 0.185 0.164 0.164
## [15337] 0.164 0.168 0.177 0.164 0.163 0.164 0.172 0.175 0.169 0.164 0.163 0.177
## [15349] 0.164 0.165 0.165 0.165 0.181 0.165 0.167 0.165 0.165 0.183 0.167 0.165
## [15361] 0.165 0.163 0.195 0.166 0.171 0.165 0.163 0.168 0.165 0.165 0.165 0.163
## [15373] 0.173 0.164 0.175 0.164 0.174 0.165 0.184 0.164 0.172 0.164 0.195 0.164
## [15385] 0.188 0.164 0.205 0.164 0.175 0.164 0.201 0.164 0.166 0.167 0.177 0.164
## [15397] 0.181 0.171 0.221 0.164 0.206 0.165 0.228 0.164 0.174 0.167 0.199 0.164
## [15409] 0.184 0.165 0.192 0.163 0.175 0.167 0.206 0.203 0.195 0.180 0.178 0.216
## [15421] 0.206 0.178 0.185 0.218 0.183 0.168 0.200 0.172 0.164 0.163 0.163 0.165
## [15433] 0.170 0.164 0.222 0.170 0.172 0.172 0.189 0.200 0.180 0.165 0.165 0.212
## [15445] 0.165 0.211 0.215 0.165 0.220 0.187 0.165 0.174 0.189 0.186 0.200 0.211
## [15457] 0.180 0.168 0.165 0.196 0.163 0.191 0.173 0.163 0.184 0.193 0.163 0.178
## [15469] 0.198 0.164 0.164 0.222 0.176 0.164 0.179 0.164 0.293 0.189 0.164 0.211
## [15481] 0.189 0.164 0.268 0.172 0.164 0.238 0.178 0.164 0.205 0.164 0.163 0.214
## [15493] 0.184 0.201 0.210 0.197 0.204 0.213 0.195 0.202 0.210 0.196 0.187 0.203
## [15505] 0.194 0.208 0.194 0.198 0.216 0.165 0.165 0.183 0.163 0.164 0.164 0.164
## [15517] 0.163 0.213 0.202 0.208 0.352 0.355 0.390 0.617 0.857 0.485 0.259 0.295
## [15529] 0.496 0.430 0.270 0.264 0.413 0.430 0.328 0.522 0.197 0.303 0.489 0.406
## [15541] 0.251 0.286 0.211 0.325 0.354 0.165 0.730 0.539 0.759 0.708 0.701 0.200
## [15553] 0.178 0.183 0.235 0.387 0.268 0.164 0.491 0.164 0.279 0.164 1.129 0.164
## [15565] 0.445 0.251 0.619 0.224 0.505 0.343 0.703 0.164 0.328 0.164 0.356 0.409
## [15577] 0.287 0.387 0.360 0.465 0.237 0.164 0.164 0.164 0.164 0.164 0.164 0.163
## [15589] 0.299 0.233 0.233 0.242 0.261 0.269 0.238 0.261 0.221 0.220 0.218 0.193
## [15601] 0.195 0.284 0.219 0.205 0.223 0.269 0.301 0.348 0.303 0.164 0.164 0.164
## [15613] 0.164 0.164 0.164 0.164 0.259 0.228 0.271 0.190 0.255 0.318 0.250 0.675
## [15625] 0.164 0.221 0.460 0.221 0.316 0.447 0.184 0.276 0.245 0.181 0.420 0.407
## [15637] 0.263 0.202 0.500 0.526 0.214 0.195 0.927 0.248 0.198 0.214 0.947 0.311
## [15649] 0.198 0.197 0.374 0.377 0.193 0.196 0.333 0.190 0.228 0.328 0.231 0.198
## [15661] 0.195 0.305 0.484 0.201 0.200 0.351 0.385 0.216 0.205 0.308 0.254 0.192
## [15673] 0.233 0.216 0.410 0.206 0.206 0.492 0.349 0.192 0.193 0.413 0.270 0.181
## [15685] 0.193 0.190 0.413 0.212 0.216 0.324 0.395 0.217 0.188 0.341 0.409 0.177
## [15697] 0.195 0.279 0.389 0.265 0.195 0.494 0.261 0.191 0.195 0.462 0.218 0.177
## [15709] 0.187 0.414 0.412 0.205 0.192 0.376 0.457 0.197 0.193 0.368 0.223 0.374
## [15721] 0.250 0.570 0.375 0.412 0.277 0.549 0.342 0.550 0.288 0.803 0.165 0.189
## [15733] 0.162 0.473 0.434 0.288 0.397 0.373 0.212 0.294 0.330 0.262 0.839 0.312
## [15745] 0.219 0.420 0.355 0.279 0.228 0.165 0.289 0.195 0.238 0.186 0.230 0.245
## [15757] 0.650 0.199 0.240 0.165 0.458 0.326 0.380 0.522 0.207 0.175 0.373 0.608
## [15769] 0.563 0.186 0.308 0.532 0.462 0.217 0.229 0.796 0.473 0.176 0.275 0.829
## [15781] 0.270 0.184 0.194 0.467 0.520 0.194 0.195 0.554 0.339 0.164 0.195 0.587
## [15793] 0.671 0.192 0.203 0.422 0.413 0.178 0.196 0.362 0.257 0.207 0.205 0.341
## [15805] 0.557 0.177 0.186 0.354 0.220 0.165 0.214 0.329 0.266 0.202 0.201 0.195
## [15817] 0.212 0.245 0.164 0.205 0.164 0.187 0.212 0.236 0.164 0.199 0.164 0.216
## [15829] 0.226 0.213 0.165 0.205 0.165 0.210 0.165 0.224 0.165 0.183 0.165 0.222
## [15841] 0.192 0.298 0.165 0.188 0.352 0.179 0.606 0.271 0.220 0.235 0.164 0.231
## [15853] 0.242 0.266 0.253 0.242 0.229 0.244 0.443 0.320 0.366 0.819 0.439 0.838
## [15865] 0.272 0.547 0.490 0.965 0.690 0.788 0.603 0.533 0.607 0.732 0.709 0.773
## [15877] 0.405 0.754 0.432 0.524 0.630 0.449 0.300 0.164 0.215 0.256 0.201 0.307
## [15889] 0.264 0.518 0.366 0.190 0.292 0.425 0.215 0.323 0.202 0.187 0.164 0.234
## [15901] 0.182 0.422 0.198 0.180 0.535 0.249 0.173 0.528 0.253 0.164 0.674 0.214
## [15913] 0.191 0.714 0.220 0.182 0.403 0.257 0.312 0.210 0.174 0.172 0.308 0.219
## [15925] 0.192 0.181 0.482 0.163 0.188 0.406 0.343 0.357 0.326 0.286 0.195 0.351
## [15937] 0.188 0.299 0.164 0.299 0.328 0.299 0.163 0.164 0.361 0.164 0.164 0.235
## [15949] 0.164 0.405 0.183 0.513 0.301 0.375 0.261 0.252 0.202 0.457 0.417 0.826
## [15961] 0.425 0.546 0.740 0.615 0.524 0.521 0.627 0.314 0.671 0.724 0.358 0.237
## [15973] 0.477 0.185 0.283 0.226 0.236 0.217 0.164 0.365 0.283 0.330 0.211 0.434
## [15985] 0.309 0.165 0.322 0.299 0.164 0.165 0.164 0.171 0.184 0.164 0.174 0.190
## [15997] 0.164 0.165 0.274 0.164 0.178 0.191 0.164 0.175 0.192 0.163 0.164 0.197
## [16009] 0.306 0.165 0.204 0.202 0.165 0.176 0.169 0.164 0.177 0.201 0.165 0.173
## [16021] 0.212 0.165 0.608 0.227 0.164 0.286 0.210 0.164 0.479 0.176 0.165 0.508
## [16033] 0.197 0.165 0.342 0.169 0.164 0.173 0.171 0.164 0.627 0.164 0.164 0.168
## [16045] 0.168 0.165 0.291 0.164 0.167 0.164 0.436 0.167 0.164 0.165 0.560 0.167
## [16057] 0.165 0.176 0.524 0.164 0.173 0.165 0.179 0.595 0.172 0.195 0.171 0.189
## [16069] 0.165 0.201 0.542 0.308 0.198 0.172 0.187 0.203 0.182 0.615 0.213 0.624
## [16081] 0.232 0.170 0.194 0.183 0.171 0.214 0.270 0.186 0.179 0.378 0.175 0.176
## [16093] 0.167 0.164 0.184 0.172 0.164 0.194 0.168 0.164 0.302 0.171 0.164 0.311
## [16105] 0.177 0.164 0.220 0.187 0.164 0.198 0.273 0.164 0.199 0.173 0.178 0.162
## [16117] 0.177 0.164 0.169 0.170 0.165 0.174 0.165 0.176 0.170 0.169 0.177 0.172
## [16129] 0.173 0.185 0.167 0.175 0.164 0.165 0.186 0.164 0.184 0.223 0.164 0.164
## [16141] 0.187 0.164 0.165 0.168 0.165 0.165 0.189 0.171 0.168 0.174 0.195 0.185
## [16153] 0.165 0.174 0.177 0.174 0.173 0.166 0.166 0.166 0.165 0.166 0.168 0.166
## [16165] 0.167 0.175 0.183 0.184 0.168 0.164 0.165 0.166 0.166 0.167 0.166 0.166
## [16177] 0.167 0.165 0.166 0.165 0.165 0.165 0.165 0.165 0.168 0.164 0.168 0.164
## [16189] 0.165 0.165 0.165 0.166 0.164 0.165 0.165 0.165 0.165 0.165 0.170 0.165
## [16201] 0.163 0.165 0.165 0.163 0.164 0.165 0.165 0.165 0.165 0.165 0.164 0.163
## [16213] 0.165 0.165 0.164 0.165 0.167 0.170 0.176 0.187 0.191 0.198 0.200 0.209
## [16225] 0.176 0.173 0.167 0.167 0.176 0.174 0.171 0.171 0.182 0.178 0.174 0.165
## [16237] 0.165 0.164 0.165 0.164 0.172 0.169 0.165 0.163 0.165 0.178 0.168 0.166
## [16249] 0.166 0.164 0.164 0.164 0.168 0.167 0.166 0.166 0.166 0.166 0.166 0.165
## [16261] 0.166 0.167 0.165 0.163 0.168 0.171 0.164 0.168 0.217 0.183 0.233 0.233
## [16273] 0.213 0.236 0.236 0.230 0.241 0.285 0.257 0.274 0.247 0.244 0.287 0.264
## [16285] 0.253 0.400 0.234 0.246 0.497 0.166 0.165 0.165 0.166 0.163 0.165 0.172
## [16297] 0.180 0.180 0.193 0.192 0.180 0.179 0.186 0.169 0.169 0.171 0.170 0.168
## [16309] 0.173 0.171 0.177 0.186 0.164 0.164 0.165 0.164 0.162 0.183 0.194 0.200
## [16321] 0.163 0.164 0.165 0.177 0.179 0.166 0.185 0.186 0.164 0.185 0.171 0.213
## [16333] 0.168 0.200 0.174 0.193 0.186 0.261 0.164 0.164 0.164 0.174 0.164 0.164
## [16345] 0.164 0.165 0.165 0.165 0.165 0.178 0.188 0.205 0.196 0.190 0.197 0.172
## [16357] 0.170 0.166 0.166 0.167 0.164 0.166 0.163 0.166 0.165 0.164 0.167 0.165
## [16369] 0.165 0.199 0.192 0.164 0.164 0.179 0.173 0.184 0.170 0.175 0.186 0.187
## [16381] 0.185 0.193 0.171 0.167 0.165 0.164 0.165 0.178 0.180 0.188 0.188 0.213
## [16393] 0.209 0.203 0.191 0.167 0.174 0.164 0.164 0.172 0.171 0.180 0.183 0.181
## [16405] 0.188 0.179 0.180 0.186 0.194 0.200 0.170 0.189 0.180 0.180 0.190 0.263
## [16417] 0.178 0.187 0.188 0.182 0.184 0.182 0.183 0.187 0.187 0.177 0.184 0.182
## [16429] 0.179 0.182 0.183 0.183 0.187 0.193 0.183 0.201 0.193 0.190 0.192 0.199
## [16441] 0.183 0.203 0.216 0.181 0.176 0.195 0.180 0.166 0.177 0.174 0.179 0.208
## [16453] 0.206 0.213 0.218 0.209 0.189 0.188 0.233 0.233 0.273 0.178 0.202 0.212
## [16465] 0.234 0.179 0.219 0.237 0.219 0.243 0.211 0.241 0.205 0.207 0.198 0.199
## [16477] 0.200 0.216 0.204 0.209 0.213 0.209 0.202 0.201 0.165 0.186 0.208 0.164
## [16489] 0.196 0.206 0.182 0.206 0.227 0.199 0.222 0.253 0.252 0.202 0.235 0.200
## [16501] 0.197 0.211 0.196 0.211 0.236 0.184 0.231 0.228 0.219 0.249 0.240 0.191
## [16513] 0.199 0.266 0.195 0.266 0.245 0.264 0.277 0.272 0.202 0.190 0.216 0.201
## [16525] 0.228 0.232 0.212 0.268 0.254 0.239 0.232 0.262 0.231 0.275 0.245 0.231
## [16537] 0.247 0.241 0.266 0.250 0.257 0.265 0.217 0.324 0.251 0.221 0.206 0.218
## [16549] 0.240 0.191 0.193 0.168 0.204 0.234 0.216 0.212 0.274 0.275 0.270 0.253
## [16561] 0.284 0.255 0.227 0.260 0.283 0.207 0.206 0.242 0.209 0.205 0.256 0.223
## [16573] 0.266 0.238 0.200 0.277 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165
## [16585] 0.165 0.165 0.165 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [16597] 0.164 0.165 0.165 0.165 0.165 0.190 0.164 0.191 0.165 0.164 0.165 0.165
## [16609] 0.165 0.165 0.181 0.183 0.165 0.178 0.165 0.187 0.165 0.188 0.165 0.184
## [16621] 0.163 0.179 0.165 0.180 0.165 0.182 0.165 0.182 0.165 0.248 0.163 0.280
## [16633] 0.165 0.413 0.165 0.481 0.164 0.351 0.420 0.429 0.165 0.407 0.435 0.434
## [16645] 0.436 0.455 0.424 0.164 0.411 0.407 0.444 0.293 0.335 0.435 0.402 0.424
## [16657] 0.384 0.417 0.403 0.362 0.196 0.221 0.164 0.230 0.164 0.188 0.190 0.180
## [16669] 0.175 0.188 0.198 0.165 0.191 0.254 0.180 0.187 0.186 0.172 0.181 0.165
## [16681] 0.187 0.163 0.196 0.163 0.182 0.215 0.182 0.205 0.182 0.165 0.173 0.168
## [16693] 0.170 0.187 0.229 0.216 0.219 0.229 0.231 0.220 0.196 0.164 0.164 0.164
## [16705] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.162 0.164 0.164
## [16717] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.166 0.165 0.164 0.164
## [16729] 0.164 0.164 0.164 0.164 0.164 0.165 0.164 0.165 0.164 0.165 0.164 0.165
## [16741] 0.164 0.165 0.164 0.164 0.164 0.165 0.164 0.165 0.164 0.165 0.164 0.165
## [16753] 0.164 0.164 0.164 0.164 0.162 0.165 0.164 0.164 0.164 0.164 0.164 0.164
## [16765] 0.164 0.165 0.164 0.165 0.164 0.165 0.164 0.165 0.164 0.164 0.164 0.164
## [16777] 0.165 0.163 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.164 0.165 0.165
## [16789] 0.164 0.164 0.164 0.164 0.164 0.179 0.164 0.169 0.165 0.165 0.164 0.165
## [16801] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.165 0.164 0.165 0.165 0.204
## [16813] 0.182 0.184 0.168 0.197 0.171 0.169 0.175 0.185 0.181 0.198 0.179 0.173
## [16825] 0.181 0.182 0.169 0.220 0.205 0.186 0.397 0.189 0.338 0.171 0.274 0.170
## [16837] 0.314 0.182 0.280 0.188 0.337 0.204 0.324 0.207 0.376 0.203 0.387 0.213
## [16849] 0.288 0.221 0.267 0.237 0.470 0.187 0.411 0.209 0.489 0.183 0.240 0.233
## [16861] 0.191 0.380 0.231 0.232 0.235 0.189 0.251 0.329 0.233 0.301 0.236 0.226
## [16873] 0.192 0.178 0.206 0.180 0.184 0.169 0.176 0.169 0.196 0.169 0.203 0.179
## [16885] 0.177 0.187 0.199 0.194 0.236 0.207 0.196 0.199 0.189 0.167 0.216 0.175
## [16897] 0.225 0.178 0.198 0.172 0.194 0.164 0.164 0.189 0.190 0.164 0.164 0.360
## [16909] 0.316 0.270 0.362 0.292 0.354 0.387 0.287 0.392 0.219 0.501 0.354 0.589
## [16921] 0.366 0.251 0.556 0.522 0.425 0.462 0.308 0.813 0.545 0.616 0.279 0.794
## [16933] 0.459 0.540 0.334 0.665 0.466 0.590 0.574 0.751 0.484 0.803 0.191 0.384
## [16945] 0.457 0.559 0.469 0.549 0.668 0.277 0.688 0.367 0.802 0.484 0.598 1.067
## [16957] 0.412 0.553 0.361 0.358 0.681 0.420 0.822 0.579 0.424 0.523 0.532 1.013
## [16969] 0.768 0.848 0.835 0.852 0.909 0.797 0.322 0.415 0.682 0.515 0.372 0.461
## [16981] 0.386 0.297 0.268 0.263 0.280 0.537 0.257 0.263 0.352 0.278 0.172 0.285
## [16993] 0.461 0.251 0.439 0.276 0.557 0.522 0.550 0.206 0.588 0.675 0.680 0.728
## [17005] 0.476 0.418 0.498 0.400 0.343 0.267 0.287 0.394 0.409 0.198 0.165 0.165
## [17017] 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.172 0.216 0.229 0.250 0.236
## [17029] 0.242 0.237 0.228 0.255 0.265 0.329 0.164 0.163 0.283 0.164 0.297 0.437
## [17041] 0.618 0.309 0.271 0.222 0.302 0.445 1.277 1.229 0.597 1.118 2.046 0.811
## [17053] 0.754 0.401 0.248 0.283 0.378 0.204 0.193 0.196 0.208 0.271 0.309 0.351
## [17065] 0.313 0.195 0.303 0.364 0.438 0.353 0.324 0.491 0.307 0.414 0.332 0.481
## [17077] 0.399 0.546 0.202 0.479 0.316 0.584 0.573 0.601 0.721 0.826 0.333 0.465
## [17089] 0.204 0.305 0.351 0.266 0.231 0.197 0.240 0.330 0.303 0.319 0.324 0.393
## [17101] 0.277 0.409 0.209 0.535 0.391 0.563 0.530 0.417 0.542 0.559 0.342 0.475
## [17113] 0.341 0.737 0.471 0.651 0.297 0.845 0.463 0.593 0.314 0.623 0.486 0.596
## [17125] 0.599 0.884 0.510 0.624 0.222 0.351 0.501 0.661 0.577 0.561 0.388 0.676
## [17137] 0.929 0.589 0.576 0.586 0.842 1.132 0.374 0.459 0.889 0.533 0.434 0.553
## [17149] 1.126 1.143 0.315 0.712 0.931 0.776 0.548 0.384 0.535 0.487 0.347 0.841
## [17161] 0.256 0.416 0.279 0.362 0.244 0.300 0.496 0.330 0.249 0.229 0.455 0.212
## [17173] 0.301 0.284 0.355 0.340 0.489 0.517 0.345 0.485 0.525 0.728 0.634 0.789
## [17185] 0.875 0.625 0.529 0.631 0.511 0.362 0.580 0.889 0.696 0.823 0.681 0.635
## [17197] 0.451 0.350 0.193 0.213 0.181 0.242 0.199 0.262 0.205 0.294 0.267 0.259
## [17209] 0.193 0.291 0.174 0.479 0.181 0.449 0.179 0.300 0.200 0.287 0.219 0.306
## [17221] 0.251 0.253 0.163 0.165 0.229 0.164 0.192 0.229 0.391 0.610 0.375 0.462
## [17233] 0.394 1.086 0.431 0.607 0.358 0.598 0.301 0.235 2.022 1.748 0.431 0.702
## [17245] 0.732 0.983 0.395 0.230 0.499 0.284 0.207 0.248 0.252 0.211 0.229 0.238
## [17257] 0.223 0.237 0.204 0.209 0.226 0.218 1.275 0.163 0.163 0.163 0.163 0.164
## [17269] 0.163 0.163 0.163 0.164 0.163 0.163 0.163 0.164 0.163 0.163 0.163 0.164
## [17281] 0.163 0.163 0.163 0.164 0.163 0.163 0.163 0.164 0.163 0.163 0.164 0.163
## [17293] 0.163 0.163 0.164 0.163 0.163 0.163 0.164 0.163 0.163 0.163 0.164 0.163
## [17305] 0.163 0.163 0.164 0.163 0.163 0.163 0.164 0.163 0.163 0.163 0.164 0.163
## [17317] 0.163 0.163 0.164 0.163 0.163 0.163 0.164 0.163 0.163 0.163 0.164 0.163
## [17329] 0.163 0.163 0.164 0.163 0.163 0.163 0.216 0.163 0.163 0.163 0.164 0.163
## [17341] 0.163 0.163 0.195 0.163 0.163 0.163 0.182 0.163 0.163 0.163 0.190 0.163
## [17353] 0.163 0.163 0.164 0.163 0.163 0.163 0.164 0.163 0.163 0.163 0.217 0.163
## [17365] 0.163 0.163 0.164 0.163 0.163 0.163 0.194 0.163 0.163 0.178 0.395 0.163
## [17377] 0.163 0.232 0.183 0.164 0.163 0.382 0.225 0.163 0.163 0.312 0.269 0.164
## [17389] 0.174 0.163 0.203 0.163 0.250 0.356 0.340 0.231 0.164 0.272 0.290 0.164
## [17401] 0.223 0.351 0.163 0.164 0.163 0.163 0.279 0.163 0.326 0.163 0.163 0.484
## [17413] 0.163 0.163 0.163 0.163 0.182 0.163 0.251 0.163 0.263 0.182 0.163 0.163
## [17425] 0.187 0.169 0.163 0.163 0.178 0.163 0.163 0.163 0.253 0.163 0.163 0.163
## [17437] 0.228 0.163 0.163 0.163 0.330 0.163 0.163 0.163 0.294 0.163 0.163 0.163
## [17449] 0.171 0.163 0.163 0.163 0.174 0.163 0.163 0.163 0.253 0.163 0.272 0.163
## [17461] 0.164 0.163 0.253 0.256 0.190 0.163 0.197 0.163 0.164 0.163 0.163 0.163
## [17473] 0.164 0.163 0.163 0.164 0.322 0.164 0.164 0.163 0.164 0.164 0.163 0.321
## [17485] 0.164 0.187 0.163 0.220 0.163 0.164 0.177 0.318 0.164 0.230 0.164 0.164
## [17497] 0.173 0.163 0.164 0.332 0.186 0.183 0.164 0.163 0.199 0.166 0.164 0.163
## [17509] 0.174 0.175 0.332 0.163 0.333 0.163 0.164 0.204 0.164 0.163 0.164 0.164
## [17521] 0.177 0.176 0.164 0.164 0.218 0.329 0.164 0.164 0.190 0.327 0.164 0.329
## [17533] 0.164 0.167 0.208 0.316 0.164 0.164 0.163 0.163 0.240 0.164 0.163 0.163
## [17545] 0.333 0.295 0.163 0.314 0.163 0.175 0.177 0.164 0.163 0.176 0.163 0.195
## [17557] 0.322 0.251 0.311 0.163 0.163 0.322 0.164 0.163 0.163 0.329 0.164 0.195
## [17569] 0.163 0.163 0.164 0.163 0.163 0.163 0.164 0.319 0.163 0.330 0.253 0.163
## [17581] 0.163 0.379 0.164 0.163 0.163 0.212 0.163 0.163 0.183 0.164 0.177 0.163
## [17593] 0.194 0.170 0.163 0.329 0.323 0.164 0.164 0.164 0.164 0.164 0.164 0.187
## [17605] 0.164 0.164 0.176 0.206 0.229 0.164 0.169 0.164 0.196 0.255 0.164 0.222
## [17617] 0.383 0.199 0.260 0.164 0.164 0.202 0.490 0.164 0.211 0.569 0.163 0.164
## [17629] 0.304 0.291 0.164 0.331 0.174 0.351 0.241 0.343 0.163 0.164 0.164 0.188
## [17641] 0.322 0.164 0.164 0.180 0.163 0.164 0.164 0.164 0.164 0.164 0.163 0.322
## [17653] 0.316 0.164 0.164 0.163 0.164 0.324 0.176 0.164 0.163 0.273 0.163 0.164
## [17665] 0.163 0.164 0.163 0.164 0.164 0.163 0.207 0.163 0.314 0.163 0.163 0.163
## [17677] 0.163 0.163 0.214 0.163 0.164 0.163 0.164 0.163 0.270 0.163 0.227 0.163
## [17689] 0.178 0.171 0.164 0.169 0.288 0.163 0.163 0.163 0.279 0.163 0.197 0.163
## [17701] 0.240 0.332 0.164 0.178 0.321 0.163 0.331 0.172 0.461 0.164 0.282 0.164
## [17713] 0.472 0.181 0.247 0.196 0.164 0.163 0.211 0.163 0.170 0.292 0.306 0.243
## [17725] 0.268 0.231 0.443 0.163 0.164 0.400 0.246 0.351 0.356 0.163 0.239 0.387
## [17737] 0.164 0.327 0.332 0.163 0.164 0.164 0.190 0.164 0.164 0.164 0.164 0.164
## [17749] 0.164 0.163 0.215 0.164 0.163 0.163 0.163 0.164 0.163 0.241 0.164 0.163
## [17761] 0.324 0.164 0.164 0.163 0.163 0.164 0.164 0.164 0.163 0.164 0.164 0.184
## [17773] 0.163 0.164 0.164 0.164 0.163 0.164 0.164 0.163 0.191 0.164 0.199 0.329
## [17785] 0.163 0.164 0.164 0.181 0.163 0.164 0.164 0.163 0.163 0.330 0.164 0.163
## [17797] 0.163 0.264 0.164 0.178 0.163 0.164 0.212 0.163 0.164 0.319 0.164 0.163
## [17809] 0.163 0.185 0.270 0.163 0.306 0.176 0.164 0.172 0.250 0.221 0.250 0.163
## [17821] 0.163 0.330 0.378 0.163 0.163 0.211 0.463 0.163 0.164 0.163 0.204 0.204
## [17833] 0.163 0.163 0.286 0.164 0.163 0.331 0.172 0.231 0.163 0.294 0.289 0.196
## [17845] 0.163 0.247 0.229 0.163 0.388 0.326 0.306 0.172 0.164 0.163 0.163 0.164
## [17857] 0.312 0.164 0.335 0.164 0.164 0.305 0.222 0.164 0.377 0.164 0.164 0.171
## [17869] 0.203 0.329 0.164 0.296 0.178 0.164 0.164 0.299 0.164 0.427 0.394 0.164
## [17881] 0.164 0.207 0.461 0.207 0.306 0.164 0.164 0.312 0.164 0.164 0.250 0.317
## [17893] 0.164 0.164 0.256 0.637 0.163 0.288 0.415 0.242 0.193 0.499 0.323 0.164
## [17905] 0.331 0.412 0.164 0.283 0.327 0.163 0.164 0.164 0.344 0.238 0.164 0.175
## [17917] 0.186 0.289 0.164 0.164 0.164 0.164 0.163 0.164 0.163 0.163 0.164 0.163
## [17929] 0.164 0.164 0.163 0.164 0.163 0.163 0.164 0.163 0.318 0.164 0.163 0.164
## [17941] 0.164 0.163 0.238 0.268 0.164 0.163 0.176 0.163 0.164 0.163 0.164 0.163
## [17953] 0.164 0.163 0.186 0.163 0.164 0.163 0.164 0.163 0.164 0.163 0.222 0.163
## [17965] 0.164 0.163 0.164 0.163 0.164 0.245 0.164 0.177 0.164 0.164 0.244 0.164
## [17977] 0.164 0.327 0.171 0.163 0.163 0.164 0.369 0.229 0.163 0.164 0.163 0.163
## [17989] 0.179 0.164 0.163 0.163 0.163 0.236 0.163 0.184 0.163 0.178 0.247 0.164
## [18001] 0.163 0.164 0.331 0.236 0.163 0.331 0.190 0.163 0.287 0.327 0.222 0.163
## [18013] 0.392 0.163 0.164 0.163 0.163 0.163 0.164 0.234 0.163 0.163 0.255 0.164
## [18025] 0.182 0.489 0.163 0.325 0.348 0.170 0.170 0.244 0.324 0.186 0.163 0.311
## [18037] 0.261 0.349 0.487 0.279 0.163 0.164 0.163 0.331 0.207 0.270 0.229 0.164
## [18049] 0.481 0.164 0.212 0.164 0.427 0.164 0.348 0.321 0.176 0.248 0.261 0.183
## [18061] 0.206 0.164 0.168 0.327 0.254 0.164 0.330 0.256 0.246 0.278 0.180 0.357
## [18073] 0.318 0.221 0.320 0.164 0.250 0.204 0.174 0.332 0.171 0.317 0.337 0.238
## [18085] 0.220 0.163 0.175 0.164 0.163 0.250 0.511 0.211 0.163 0.163 0.164 0.194
## [18097] 0.163 0.163 0.471 0.164 0.164 0.203 0.294 0.164 0.222 0.296 0.163 0.164
## [18109] 0.164 0.163 0.164 0.214 0.164 0.164 0.327 0.164 0.322 0.164 0.163 0.164
## [18121] 0.163 0.164 0.198 0.164 0.164 0.171 0.164 0.164 0.184 0.164 0.164 0.163
## [18133] 0.164 0.164 0.176 0.164 0.164 0.301 0.239 0.164 0.164 0.166 0.164 0.164
## [18145] 0.177 0.164 0.164 0.164 0.163 0.164 0.164 0.164 0.163 0.164 0.164 0.197
## [18157] 0.163 0.164 0.164 0.194 0.163 0.164 0.164 0.313 0.163 0.164 0.164 0.323
## [18169] 0.163 0.164 0.164 0.179 0.163 0.164 0.164 0.163 0.164 0.164 0.172 0.163
## [18181] 0.164 0.164 0.164 0.250 0.263 0.164 0.200 0.163 0.164 0.215 0.164 0.163
## [18193] 0.164 0.335 0.369 0.163 0.164 0.163 0.295 0.238 0.164 0.163 0.214 0.164
## [18205] 0.164 0.332 0.164 0.164 0.164 0.206 0.475 0.164 0.164 0.233 0.163 0.164
## [18217] 0.164 0.164 0.163 0.164 0.164 0.164 0.183 0.164 0.164 0.227 0.163 0.164
## [18229] 0.164 0.283 0.163 0.164 0.164 0.313 0.204 0.164 0.164 0.179 0.164 0.164
## [18241] 0.164 0.190 0.179 0.164 0.164 0.186 0.345 0.164 0.164 0.197 0.164 0.164
## [18253] 0.164 0.398 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.250
## [18265] 0.164 0.164 0.164 0.175 0.164 0.164 0.164 0.164 0.333 0.164 0.164 0.329
## [18277] 0.270 0.164 0.164 0.195 0.164 0.164 0.164 0.234 0.164 0.164 0.164 0.201
## [18289] 0.164 0.164 0.164 0.219 0.381 0.164 0.225 0.177 0.480 0.164 0.231 0.439
## [18301] 0.164 0.164 0.260 0.323 0.164 0.164 0.481 0.164 0.164 0.221 0.222 0.164
## [18313] 0.164 0.195 0.181 0.164 0.164 0.164 0.402 0.164 0.164 0.328 0.164 0.164
## [18325] 0.304 0.164 0.188 0.164 0.326 0.316 0.164 0.313 0.163 0.243 0.164 0.163
## [18337] 0.170 0.175 0.163 0.350 0.163 0.163 0.330 0.183 0.163 0.214 0.164 0.163
## [18349] 0.164 0.164 0.163 0.164 0.164 0.163 0.164 0.164 0.163 0.163 0.173 0.314
## [18361] 0.163 0.163 0.163 0.163 0.163 0.344 0.163 0.193 0.163 0.232 0.163 0.164
## [18373] 0.163 0.163 0.324 0.163 0.163 0.220 0.163 0.163 0.174 0.164 0.163 0.163
## [18385] 0.164 0.164 0.163 0.163 0.164 0.304 0.163 0.163 0.185 0.163 0.324 0.163
## [18397] 0.385 0.163 0.164 0.332 0.167 0.163 0.164 0.325 0.179 0.163 0.164 0.163
## [18409] 0.163 0.246 0.164 0.163 0.196 0.164 0.227 0.163 0.224 0.205 0.163 0.163
## [18421] 0.164 0.163 0.302 0.179 0.164 0.163 0.253 0.257 0.190 0.164 0.194 0.164
## [18433] 0.430 0.163 0.163 0.397 0.164 0.183 0.163 0.163 0.164 0.163 0.167 0.163
## [18445] 0.332 0.190 0.315 0.339 0.164 0.360 0.331 0.253 0.164 0.211 0.163 0.164
## [18457] 0.175 0.164 0.163 0.163 0.211 0.184 0.164 0.163 0.318 0.163 0.164 0.164
## [18469] 0.163 0.163 0.194 0.163 0.163 0.186 0.171 0.163 0.163 0.237 0.163 0.163
## [18481] 0.163 0.244 0.163 0.323 0.163 0.163 0.163 0.500 0.163 0.163 0.250 0.163
## [18493] 0.163 0.219 0.164 0.189 0.323 0.215 0.193 0.189 0.238 0.330 0.164 0.164
## [18505] 0.329 0.163 0.164 0.164 0.331 0.322 0.164 0.163 0.164 0.163 0.163 0.164
## [18517] 0.164 0.186 0.174 0.175 0.204 0.169 0.170 0.166 0.164 0.183 0.164 0.238
## [18529] 0.207 0.172 0.164 0.170 0.164 0.164 0.168 0.164 0.164 0.172 0.170 0.164
## [18541] 0.164 0.229 0.175 0.176 0.164 0.316 0.169 0.172 0.164 0.456 0.167 0.164
## [18553] 0.164 0.366 0.168 0.164 0.164 0.190 0.164 0.164 0.164 0.186 0.164 0.164
## [18565] 0.163 0.164 0.331 0.164 0.163 0.164 0.163 0.164 0.175 0.268 0.163 0.163
## [18577] 0.163 0.189 0.164 0.192 0.163 0.164 0.169 0.327 0.186 0.174 0.164 0.174
## [18589] 0.332 0.164 0.164 0.198 0.164 0.176 0.255 0.381 0.164 0.164 0.164 0.163
## [18601] 0.164 0.164 0.164 0.163 0.230 0.164 0.163 0.164 0.164 0.164 0.163 0.258
## [18613] 0.164 0.173 0.174 0.331 0.173 0.164 0.163 0.164 0.164 0.164 0.164 0.164
## [18625] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.329 0.164 0.164 0.164
## [18637] 0.164 0.188 0.211 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [18649] 0.173 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [18661] 0.183 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [18673] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [18685] 0.164 0.164 0.201 0.164 0.179 0.164 0.164 0.164 0.164 0.163 0.164 0.164
## [18697] 0.164 0.164 0.164 0.164 0.186 0.164 0.164 0.164 0.164 0.164 0.163 0.164
## [18709] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.163 0.164 0.163 0.164 0.164
## [18721] 0.163 0.164 0.164 0.164 0.163 0.164 0.164 0.164 0.163 0.164 0.164 0.164
## [18733] 0.163 0.164 0.164 0.164 0.163 0.164 0.164 0.164 0.163 0.164 0.164 0.163
## [18745] 0.164 0.164 0.163 0.164 0.164 0.164 0.163 0.164 0.164 0.164 0.164 0.164
## [18757] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.163 0.164 0.164 0.164 0.163
## [18769] 0.164 0.164 0.164 0.172 0.164 0.164 0.164 0.163 0.164 0.164 0.164 0.171
## [18781] 0.164 0.164 0.164 0.384 0.164 0.164 0.164 0.163 0.164 0.164 0.163 0.164
## [18793] 0.164 0.163 0.164 0.233 0.163 0.164 0.164 0.163 0.164 0.164 0.163 0.164
## [18805] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [18817] 0.329 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [18829] 0.164 0.164 0.164 0.164 0.326 0.164 0.164 0.250 0.164 0.164 0.249 0.164
## [18841] 0.164 0.399 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [18853] 0.164 0.164 0.164 0.164 0.164 0.164 0.163 0.164 0.164 0.294 0.164 0.164
## [18865] 0.164 0.164 0.164 0.164 0.163 0.164 0.164 0.164 0.164 0.164 0.164 0.163
## [18877] 0.163 0.164 0.164 0.163 0.163 0.164 0.164 0.163 0.163 0.164 0.164 0.163
## [18889] 0.163 0.164 0.164 0.163 0.163 0.164 0.164 0.163 0.163 0.164 0.164 0.163
## [18901] 0.249 0.164 0.164 0.163 0.164 0.164 0.164 0.163 0.164 0.164 0.164 0.163
## [18913] 0.164 0.164 0.164 0.163 0.164 0.164 0.164 0.163 0.164 0.164 0.164 0.163
## [18925] 0.164 0.164 0.164 0.222 0.332 0.164 0.164 0.163 0.163 0.164 0.164 0.163
## [18937] 0.163 0.164 0.164 0.163 0.188 0.164 0.164 0.225 0.164 0.164 0.164 0.164
## [18949] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.288 0.164 0.164 0.164 0.163
## [18961] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.163
## [18973] 0.164 0.164 0.164 0.163 0.164 0.164 0.164 0.163 0.164 0.164 0.164 0.163
## [18985] 0.164 0.183 0.164 0.163 0.164 0.174 0.164 0.163 0.164 0.164 0.164 0.164
## [18997] 0.164 0.176 0.164 0.164 0.164 0.332 0.164 0.164 0.164 0.164 0.164 0.324
## [19009] 0.164 0.164 0.164 0.164 0.164 0.164 0.186 0.164 0.164 0.164 0.164 0.211
## [19021] 0.163 0.164 0.164 0.164 0.164 0.332 0.164 0.254 0.168 0.164 0.164 0.164
## [19033] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [19045] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [19057] 0.164 0.171 0.164 0.164 0.164 0.211 0.164 0.164 0.164 0.309 0.164 0.164
## [19069] 0.163 0.163 0.164 0.164 0.163 0.163 0.164 0.164 0.164 0.163 0.164 0.164
## [19081] 0.163 0.163 0.164 0.164 0.164 0.163 0.164 0.165 0.164 0.164 0.164 0.164
## [19093] 0.164 0.164 0.164 0.164 0.164 0.163 0.164 0.164 0.165 0.163 0.164 0.164
## [19105] 0.163 0.164 0.164 0.163 0.164 0.164 0.163 0.164 0.164 0.164 0.172 0.164
## [19117] 0.164 0.164 0.171 0.164 0.164 0.164 0.164 0.164 0.175 0.164 0.164 0.433
## [19129] 0.326 0.164 0.227 0.324 0.164 0.341 0.230 0.164 0.165 0.282 0.315 0.164
## [19141] 0.189 0.326 0.164 0.436 0.164 0.164 0.328 0.164 0.164 0.326 0.311 0.164
## [19153] 0.164 0.263 0.164 0.328 0.270 0.321 0.164 0.278 0.164 0.251 0.164 0.185
## [19165] 0.352 0.164 0.164 0.298 0.164 0.163 0.267 0.385 0.298 0.323 0.227 0.332
## [19177] 0.164 0.313 0.327 0.164 0.164 0.323 0.323 0.164 0.406 0.348 0.164 0.164
## [19189] 0.164 0.164 0.164 0.164 0.164 0.164 0.326 0.164 0.164 0.164 0.164 0.164
## [19201] 0.164 0.227 0.164 0.164 0.194 0.164 0.228 0.169 0.164 0.331 0.182 0.164
## [19213] 0.169 0.165 0.179 0.164 0.164 0.169 0.164 0.164 0.165 0.341 0.164 0.164
## [19225] 0.165 0.163 0.164 0.164 0.195 0.164 0.164 0.164 0.208 0.164 0.164 0.163
## [19237] 0.163 0.168 0.164 0.163 0.328 0.164 0.164 0.175 0.216 0.164 0.164 0.164
## [19249] 0.210 0.164 0.311 0.164 0.252 0.164 0.266 0.164 0.174 0.291 0.200 0.164
## [19261] 0.170 0.163 0.163 0.238 0.164 0.163 0.191 0.165 0.217 0.163 0.164 0.164
## [19273] 0.179 0.169 0.298 0.329 0.220 0.164 0.248 0.164 0.178 0.164 0.163 0.164
## [19285] 0.327 0.164 0.245 0.164 0.289 0.329 0.164 0.164 0.323 0.163 0.176 0.332
## [19297] 0.163 0.163 0.330 0.316 0.482 0.331 0.306 0.239 0.164 0.164 0.163 0.164
## [19309] 0.190 0.163 0.224 0.164 0.164 0.184 0.310 0.164 0.164 0.180 0.164 0.310
## [19321] 0.316 0.325 0.304 0.164 0.164 0.216 0.164 0.252 0.211 0.164 0.268 0.318
## [19333] 0.275 0.164 0.163 0.174 0.195 0.164 0.261 0.164 0.242 0.164 0.176 0.164
## [19345] 0.163 0.164 0.168 0.164 0.163 0.164 0.180 0.164 0.222 0.164 0.190 0.311
## [19357] 0.222 0.316 0.240 0.332 0.163 0.328 0.323 0.196 0.244 0.247 0.340 0.163
## [19369] 0.163 0.225 0.331 0.211 0.240 0.164 0.331 0.239 0.235 0.260 0.265 0.165
## [19381] 0.163 0.244 0.253 0.164 0.267 0.287 0.165 0.164 0.164 0.200 0.165 0.278
## [19393] 0.164 0.165 0.237 0.164 0.164 0.165 0.164 0.163 0.164 0.163 0.164 0.260
## [19405] 0.164 0.164 0.164 0.254 0.164 0.163 0.164 0.239 0.199 0.164 0.163 0.164
## [19417] 0.163 0.164 0.163 0.164 0.163 0.164 0.163 0.164 0.163 0.164 0.163 0.174
## [19429] 0.163 0.163 0.164 0.304 0.163 0.163 0.163 0.164 0.164 0.163 0.163 0.163
## [19441] 0.163 0.163 0.163 0.164 0.164 0.333 0.309 0.164 0.164 0.206 0.217 0.203
## [19453] 0.246 0.245 0.164 0.323 0.164 0.167 0.164 0.163 0.168 0.163 0.164 0.163
## [19465] 0.164 0.163 0.164 0.163 0.164 0.163 0.164 0.163 0.164 0.163 0.164 0.163
## [19477] 0.164 0.163 0.164 0.163 0.164 0.163 0.163 0.164 0.163 0.164 0.163 0.164
## [19489] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.165 0.163 0.164
## [19501] 0.164 0.165 0.186 0.164 0.164 0.165 0.163 0.164 0.164 0.164 0.163 0.164
## [19513] 0.164 0.164 0.163 0.164 0.164 0.164 0.163 0.164 0.164 0.164 0.163 0.164
## [19525] 0.164 0.164 0.163 0.164 0.164 0.164 0.163 0.164 0.328 0.164 0.290 0.164
## [19537] 0.328 0.164 0.164 0.164 0.164 0.164 0.164 0.175 0.164 0.164 0.167 0.222
## [19549] 0.284 0.165 0.279 0.164 0.215 0.317 0.164 0.230 0.204 0.190 0.296 0.312
## [19561] 0.215 0.295 0.268 0.164 0.163 0.332 0.315 0.317 0.163 0.164 0.278 0.211
## [19573] 0.169 0.164 0.163 0.222 0.163 0.164 0.163 0.164 0.328 0.164 0.163 0.278
## [19585] 0.253 0.164 0.163 0.164 0.166 0.164 0.183 0.163 0.163 0.164 0.278 0.331
## [19597] 0.164 0.267 0.164 0.164 0.164 0.251 0.164 0.323 0.164 0.164 0.237 0.164
## [19609] 0.164 0.164 0.164 0.164 0.183 0.164 0.164 0.302 0.164 0.330 0.172 0.164
## [19621] 0.164 0.229 0.300 0.164 0.164 0.235 0.164 0.164 0.164 0.193 0.164 0.222
## [19633] 0.286 0.164 0.164 0.163 0.252 0.236 0.164 0.163 0.164 0.323 0.163 0.163
## [19645] 0.164 0.190 0.398 0.251 0.165 0.164 0.164 0.181 0.164 0.255 0.164 0.163
## [19657] 0.165 0.165 0.164 0.164 0.165 0.181 0.164 0.164 0.163 0.174 0.164 0.164
## [19669] 0.178 0.164 0.164 0.170 0.164 0.164 0.163 0.185 0.164 0.165 0.163 0.164
## [19681] 0.164 0.165 0.163 0.164 0.164 0.165 0.163 0.164 0.164 0.165 0.163 0.164
## [19693] 0.164 0.164 0.163 0.164 0.164 0.164 0.163 0.164 0.164 0.164 0.163 0.164
## [19705] 0.164 0.164 0.163 0.164 0.164 0.164 0.163 0.164 0.164 0.164 0.163 0.176
## [19717] 0.164 0.164 0.163 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.163 0.164
## [19729] 0.164 0.164 0.163 0.164 0.164 0.165 0.163 0.260 0.164 0.164 0.163 0.164
## [19741] 0.214 0.164 0.164 0.164 0.170 0.164 0.164 0.164 0.164 0.292 0.164 0.263
## [19753] 0.164 0.164 0.188 0.211 0.319 0.332 0.284 0.164 0.190 0.163 0.200 0.218
## [19765] 0.205 0.238 0.164 0.163 0.226 0.185 0.177 0.234 0.185 0.164 0.253 0.164
## [19777] 0.215 0.278 0.164 0.176 0.222 0.164 0.164 0.164 0.237 0.164 0.164 0.164
## [19789] 0.164 0.164 0.164 0.164 0.307 0.164 0.164 0.254 0.164 0.164 0.164 0.164
## [19801] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.238 0.164 0.164
## [19813] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [19825] 0.164 0.164 0.164 0.164 0.164 0.165 0.164 0.164 0.164 0.164 0.164 0.165
## [19837] 0.164 0.164 0.165 0.163 0.164 0.164 0.165 0.164 0.164 0.165 0.163 0.164
## [19849] 0.164 0.163 0.164 0.164 0.164 0.163 0.164 0.164 0.304 0.163 0.164 0.164
## [19861] 0.222 0.164 0.164 0.360 0.164 0.164 0.165 0.170 0.164 0.164 0.165 0.243
## [19873] 0.164 0.164 0.165 0.163 0.164 0.164 0.165 0.163 0.164 0.164 0.179 0.163
## [19885] 0.164 0.164 0.163 0.163 0.164 0.164 0.163 0.163 0.164 0.164 0.163 0.163
## [19897] 0.164 0.164 0.163 0.163 0.164 0.164 0.163 0.163 0.164 0.164 0.163 0.163
## [19909] 0.164 0.164 0.163 0.181 0.164 0.164 0.310 0.202 0.164 0.164 0.318 0.164
## [19921] 0.279 0.164 0.165 0.228 0.163 0.164 0.164 0.181 0.205 0.164 0.169 0.168
## [19933] 0.164 0.193 0.163 0.179 0.209 0.163 0.163 0.163 0.163 0.163 0.163 0.163
## [19945] 0.163 0.163 0.163 0.163 0.163 0.163 0.225 0.188 0.163 0.163 0.163 0.163
## [19957] 0.163 0.163 0.163 0.224 0.164 0.163 0.196 0.164 0.163 0.163 0.173 0.166
## [19969] 0.163 0.163 0.164 0.163 0.163 0.163 0.331 0.164 0.163 0.163 0.163 0.164
## [19981] 0.163 0.163 0.237 0.163 0.164 0.163 0.163 0.164 0.163 0.163 0.163 0.163
## [19993] 0.164 0.163 0.163 0.163 0.164 0.163 0.163 0.163 0.189 0.164 0.163 0.163
## [20005] 0.207 0.164 0.163 0.163 0.212 0.163 0.163 0.163 0.164 0.263 0.309 0.164
## [20017] 0.231 0.163 0.189 0.329 0.259 0.163 0.164 0.328 0.238 0.328 0.164 0.270
## [20029] 0.164 0.252 0.165 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.165 0.164
## [20041] 0.164 0.165 0.163 0.164 0.164 0.165 0.211 0.164 0.164 0.165 0.165 0.165
## [20053] 0.165 0.165 0.165 0.165 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [20065] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.181 0.166 0.193
## [20077] 0.165 0.164 0.165 0.166 0.164 0.165 0.168 0.165 0.165 0.169 0.165 0.168
## [20089] 0.165 0.171 0.165 0.179 0.179 0.175 0.165 0.168 0.165 0.173 0.178 0.164
## [20101] 0.173 0.164 0.178 0.164 0.176 0.164 0.171 0.164 0.168 0.164 0.169 0.164
## [20113] 0.169 0.164 0.170 0.164 0.172 0.164 0.171 0.164 0.173 0.164 0.170 0.164
## [20125] 0.170 0.164 0.166 0.164 0.164 0.164 0.164 0.164 0.164 0.181 0.164 0.166
## [20137] 0.164 0.165 0.164 0.169 0.168 0.168 0.169 0.166 0.168 0.166 0.171 0.167
## [20149] 0.168 0.165 0.168 0.165 0.168 0.165 0.168 0.165 0.168 0.165 0.167 0.165
## [20161] 0.166 0.167 0.167 0.165 0.166 0.166 0.171 0.167 0.168 0.165 0.167 0.165
## [20173] 0.167 0.165 0.168 0.165 0.171 0.171 0.164 0.164 0.164 0.164 0.165 0.164
## [20185] 0.164 0.165 0.164 0.164 0.165 0.164 0.164 0.164 0.164 0.164 0.163 0.165
## [20197] 0.164 0.164 0.165 0.164 0.164 0.164 0.164 0.165 0.164 0.164 0.165 0.164
## [20209] 0.165 0.164 0.216 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [20221] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [20233] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [20245] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [20257] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [20269] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [20281] 0.165 0.165 0.167 0.165 0.165 0.165 0.165 0.167 0.165 0.165 0.165 0.165
## [20293] 0.165 0.187 0.165 0.165 0.177 0.165 0.172 0.165 0.166 0.165 0.165 0.164
## [20305] 0.164 0.164 0.165 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [20317] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [20329] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.165 0.165 0.165 0.165
## [20341] 0.165 0.165 0.165 0.165 0.165 0.165 0.170 0.184 0.165 0.180 0.176 0.165
## [20353] 0.178 0.191 0.165 0.190 0.176 0.165 0.174 0.164 0.165 0.177 0.170 0.165
## [20365] 0.181 0.165 0.165 0.179 0.165 0.177 0.165 0.176 0.165 0.169 0.165 0.168
## [20377] 0.165 0.176 0.164 0.176 0.164 0.176 0.164 0.177 0.164 0.172 0.164 0.170
## [20389] 0.164 0.172 0.164 0.173 0.164 0.171 0.164 0.174 0.164 0.173 0.164 0.170
## [20401] 0.164 0.171 0.164 0.171 0.164 0.172 0.164 0.164 0.164 0.164 0.164 0.164
## [20413] 0.187 0.164 0.172 0.164 0.167 0.164 0.165 0.164 0.169 0.164 0.166 0.164
## [20425] 0.167 0.164 0.172 0.164 0.168 0.173 0.164 0.166 0.164 0.168 0.164 0.173
## [20437] 0.173 0.167 0.164 0.170 0.164 0.170 0.168 0.164 0.167 0.164 0.169 0.164
## [20449] 0.171 0.180 0.164 0.164 0.165 0.165 0.165 0.164 0.165 0.165 0.164 0.165
## [20461] 0.164 0.165 0.165 0.164 0.164 0.165 0.165 0.164 0.165 0.164 0.165 0.165
## [20473] 0.164 0.164 0.165 0.164 0.164 0.165 0.164 0.164 0.165 0.165 0.164 0.165
## [20485] 0.165 0.164 0.165 0.164 0.164 0.164 0.164 0.163 0.164 0.164 0.164 0.164
## [20497] 0.164 0.164 0.164 0.164 0.165 0.164 0.165 0.164 0.164 0.164 0.164 0.164
## [20509] 0.164 0.164 0.164 0.164 0.165 0.164 0.164 0.164 0.164 0.164 0.165 0.164
## [20521] 0.164 0.164 0.164 0.164 0.163 0.164 0.164 0.164 0.164 0.168 0.164 0.164
## [20533] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [20545] 0.164 0.164 0.164 0.165 0.165 0.165 0.165 0.164 0.164 0.165 0.165 0.164
## [20557] 0.165 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [20569] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [20581] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.165 0.164 0.164
## [20593] 0.164 0.164 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.165
## [20605] 0.165 0.165 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [20617] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [20629] 0.164 0.164 0.165 0.164 0.164 0.165 0.164 0.164 0.165 0.164 0.164 0.165
## [20641] 0.164 0.164 0.165 0.164 0.164 0.165 0.164 0.165 0.165 0.164 0.164 0.165
## [20653] 0.164 0.165 0.164 0.164 0.165 0.164 0.164 0.165 0.165 0.164 0.164 0.164
## [20665] 0.164 0.162 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [20677] 0.164 0.164 0.165 0.164 0.164 0.163 0.164 0.164 0.164 0.164 0.164 0.164
## [20689] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [20701] 0.176 0.186 0.195 0.191 0.199 0.184 0.209 0.239 0.330 0.183 0.208 0.205
## [20713] 0.164 0.194 0.164 0.183 0.164 0.175 0.204 0.190 0.242 0.304 0.276 0.231
## [20725] 0.167 0.167 0.168 0.168 0.168 0.170 0.164 0.253 0.215 0.242 0.245 0.223
## [20737] 0.195 0.197 0.221 0.227 0.189 0.242 0.164 0.226 0.189 0.172 0.192 0.208
## [20749] 0.168 0.192 0.208 0.168 0.234 0.192 0.177 0.261 0.236 0.213 0.165 0.164
## [20761] 0.164 0.165 0.164 0.164 0.164 0.165 0.242 0.279 0.187 0.271 0.208 0.172
## [20773] 0.165 0.165 0.164 0.164 0.165 0.165 0.163 0.165 0.165 0.504 0.220 0.165
## [20785] 0.165 0.165 0.165 0.165 0.165 0.165 0.165 0.164 0.164 0.164 0.164 0.164
## [20797] 0.163 0.164 0.164 0.222 0.303 0.346 0.165 0.164 0.165 0.166 0.164 0.164
## [20809] 0.164 0.164 0.164 0.163 0.163 0.163 0.164 0.164 0.164 0.164 0.164 0.164
## [20821] 0.164 0.164 0.164 0.163 0.163 0.163 0.185 0.163 0.163 0.179 0.179 0.178
## [20833] 0.178 0.171 0.166 0.163 0.163 0.165 0.163 0.345 0.164 0.164 0.164 0.164
## [20845] 0.225 0.412 0.165 0.214 0.240 0.165 0.165 0.236 0.165 0.172 0.177 0.216
## [20857] 0.188 0.166 0.195 0.165 0.239 0.305 0.258 0.230 0.196 0.575 0.199 0.182
## [20869] 0.235 0.164 0.295 0.164 0.165 0.214 0.208 0.165 0.164 0.164 0.164 0.164
## [20881] 0.164 0.164 0.164 0.162 0.162 0.162 0.162 0.162 0.164 0.183 0.181 0.173
## [20893] 0.172 0.174 0.218 0.388 0.164 0.236 0.264 0.189 0.194 0.164 0.164 0.164
## [20905] 0.164 0.164 0.164 0.164 0.164 0.177 0.297 0.235 0.195 0.171 0.164 0.164
## [20917] 0.165 0.165 0.166 0.164 0.165 0.165 0.165 0.165 0.165 0.164 0.165 0.164
## [20929] 0.164 0.165 0.165 0.165 0.202 0.164 0.165 0.164 0.165 0.165 0.165 0.165
## [20941] 0.165 0.165 0.165 0.162 0.162 0.193 0.179 0.178 0.170 0.164 0.164 0.165
## [20953] 0.165 0.164 0.165 0.201 0.314 0.339 0.233 0.320 0.524 0.164 0.217 0.174
## [20965] 0.164 0.164 0.423 0.214 0.226 0.200 0.198 0.167 0.186 0.314 0.211 0.164
## [20977] 0.319 0.328 0.164 0.192 0.197 0.164 0.164 0.222 0.326 0.211 0.165 0.203
## [20989] 0.179 0.193 0.218 0.225 0.227 0.205 0.169 0.260 0.199 0.164 0.303 0.311
## [21001] 0.193 0.298 0.221 0.278 0.171 0.191 0.385 0.320 0.344 0.225 0.268 0.257
## [21013] 0.300 0.344 0.332 0.357 0.323 0.227 0.236 0.233 0.258 0.241 0.231 0.263
## [21025] 0.262 0.276 0.273 0.269 0.272 0.273 0.278 0.270 0.275 0.251 0.219 0.274
## [21037] 0.276 0.275 0.287 0.284 0.286 0.362 0.277 0.275 0.272 0.181 0.178 0.275
## [21049] 0.261 0.264 0.256 0.261 0.229 0.229 0.224 0.255 0.171 0.177 0.180 0.181
## [21061] 0.192 0.168 0.181 0.189 0.169 0.181 0.170 0.175 0.182 0.178 0.180 0.186
## [21073] 0.175 0.172 0.167 0.169 0.165 0.164 0.167 0.168 0.165 0.165 0.170 0.166
## [21085] 0.165 0.167 0.172 0.165 0.177 0.174 0.183 0.200 0.185 0.169 0.179 0.166
## [21097] 0.174 0.167 0.174 0.170 0.164 0.193 0.200 0.177 0.175 0.177 0.183 0.177
## [21109] 0.187 0.176 0.180 0.181 0.173 0.168 0.176 0.173 0.166 0.168 0.168 0.165
## [21121] 0.167 0.168 0.164 0.162 0.164 0.167 0.164 0.167 0.164 0.166 0.166 0.166
## [21133] 0.166 0.172 0.166 0.164 0.166 0.164 0.170 0.178 0.164 0.167 0.166 0.176
## [21145] 0.164 0.165 0.168 0.166 0.164 0.168 0.164 0.166 0.164 0.164 0.164 0.166
## [21157] 0.164 0.164 0.164 0.166 0.168 0.164 0.167 0.168 0.169 0.167 0.199 0.170
## [21169] 0.166 0.170 0.174 0.166 0.206 0.165 0.177 0.167 0.164 0.164 0.163 0.163
## [21181] 0.164 0.321 0.273 0.272 0.270 0.270 0.262 0.273 0.270 0.250 0.272 0.269
## [21193] 0.207 0.268 0.271 0.271 0.251 0.233 0.273 0.269 0.269 0.247 0.266 0.271
## [21205] 0.264 0.294 0.225 0.242 0.229 0.221 0.274 0.215 0.263 0.224 0.186 0.174
## [21217] 0.170 0.264 0.301 0.241 0.230 0.265 0.238 0.281 0.280 0.176 0.176 0.282
## [21229] 0.270 0.268 0.257 0.266 0.258 0.271 0.270 0.271 0.172 0.165 0.164 0.164
## [21241] 0.164 0.164 0.164 0.164 0.164 0.165 0.164 0.167 0.168 0.164 0.164 0.169
## [21253] 0.164 0.168 0.167 0.168 0.164 0.168 0.164 0.164 0.164 0.164 0.164 0.164
## [21265] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.167 0.164 0.173 0.175
## [21277] 0.167 0.169 0.165 0.181 0.182 0.162 0.168 0.162 0.163 0.162 0.179 0.163
## [21289] 0.170 0.162 0.168 0.167 0.167 0.170 0.167 0.166 0.168 0.165 0.166 0.165
## [21301] 0.165 0.165 0.164 0.177 0.167 0.169 0.165 0.168 0.169 0.162 0.167 0.165
## [21313] 0.165 0.172 0.165 0.180 0.188 0.171 0.173 0.164 0.165 0.162 0.162 0.162
## [21325] 0.162 0.172 0.204 0.164 0.339 0.239 0.196 0.165 0.179 0.168 0.169 0.177
## [21337] 0.192 0.176 0.165 0.164 0.165 0.165 0.196 0.162 0.162 0.238 0.273 0.163
## [21349] 0.165 0.258 0.163 0.165 0.265 0.163 0.163 0.299 0.163 0.236 0.190 0.254
## [21361] 0.165 0.291 0.233 0.242 0.243 0.306 0.228 0.163 0.247 0.252 0.231 0.277
## [21373] 0.266 0.257 0.280 0.290 0.164 0.291 0.267 0.164 0.287 0.164 0.164 0.164
## [21385] 0.215 0.164 0.205 0.209 0.259 0.220 0.314 0.285 0.222 0.182 0.328 0.219
## [21397] 0.240 0.369 0.330 0.349 0.181 0.165 0.202 0.163 0.186 0.163 0.182 0.163
## [21409] 0.163 0.165 0.176 0.163 0.163 0.165 0.177 0.163 0.163 0.165 0.180 0.163
## [21421] 0.165 0.162 0.164 0.165 0.165 0.165 0.164 0.164 0.165 0.162 0.173 0.165
## [21433] 0.165 0.162 0.164 0.164 0.165 0.162 0.164 0.164 0.165 0.164 0.164 0.164
## [21445] 0.165 0.162 0.164 0.164 0.165 0.173 0.164 0.164 0.165 0.162 0.164 0.164
## [21457] 0.165 0.165 0.164 0.164 0.165 0.165 0.164 0.164 0.165 0.165 0.164 0.164
## [21469] 0.165 0.164 0.164 0.164 0.165 0.165 0.164 0.164 0.165 0.165 0.164 0.164
## [21481] 0.165 0.197 0.164 0.164 0.165 0.170 0.164 0.164 0.165 0.165 0.164 0.164
## [21493] 0.165 0.165 0.164 0.164 0.165 0.230 0.164 0.164 0.165 0.181 0.164 0.164
## [21505] 0.165 0.163 0.164 0.164 0.169 0.162 0.164 0.164 0.165 0.165 0.164 0.223
## [21517] 0.166 0.165 0.164 0.164 0.165 0.165 0.164 0.174 0.165 0.165 0.164 0.183
## [21529] 0.165 0.165 0.164 0.168 0.167 0.190 0.165 0.164 0.164 0.165 0.172 0.165
## [21541] 0.183 0.164 0.165 0.172 0.165 0.229 0.164 0.165 0.170 0.165 0.222 0.164
## [21553] 0.165 0.178 0.165 0.261 0.165 0.165 0.168 0.165 0.221 0.164 0.171 0.165
## [21565] 0.220 0.164 0.171 0.165 0.174 0.165 0.171 0.165 0.178 0.165 0.171 0.165
## [21577] 0.168 0.165 0.165 0.170 0.165 0.164 0.165 0.165 0.163 0.165 0.164 0.165
## [21589] 0.162 0.165 0.166 0.165 0.162 0.167 0.164 0.165 0.167 0.164 0.165 0.165
## [21601] 0.164 0.165 0.167 0.165 0.164 0.165 0.165 0.165 0.164 0.165 0.165 0.165
## [21613] 0.164 0.165 0.165 0.164 0.164 0.165 0.164 0.164 0.164 0.165 0.165 0.164
## [21625] 0.164 0.165 0.164 0.164 0.164 0.165 0.164 0.164 0.164 0.165 0.165 0.164
## [21637] 0.164 0.165 0.165 0.224 0.164 0.207 0.164 0.164 0.178 0.164 0.163 0.222
## [21649] 0.164 0.164 0.170 0.164 0.164 0.229 0.164 0.164 0.234 0.164 0.164 0.219
## [21661] 0.164 0.164 0.172 0.164 0.164 0.165 0.164 0.164 0.165 0.164 0.164 0.165
## [21673] 0.164 0.181 0.181 0.164 0.164 0.222 0.164 0.164 0.220 0.164 0.164 0.188
## [21685] 0.171 0.164 0.165 0.164 0.164 0.249 0.164 0.164 0.191 0.164 0.164 0.178
## [21697] 0.170 0.164 0.199 0.169 0.164 0.195 0.164 0.164 0.252 0.164 0.169 0.257
## [21709] 0.282 0.164 0.206 0.164 0.164 0.201 0.164 0.164 0.176 0.164 0.164 0.213
## [21721] 0.164 0.164 0.176 0.164 0.164 0.172 0.164 0.164 0.162 0.164 0.164 0.173
## [21733] 0.164 0.164 0.162 0.168 0.162 0.164 0.164 0.162 0.164 0.164 0.162 0.164
## [21745] 0.162 0.164 0.162 0.162 0.164 0.229 0.162 0.164 0.216 0.162 0.164 0.258
## [21757] 0.165 0.164 0.166 0.175 0.189 0.254 0.165 0.191 0.164 0.165 0.175 0.164
## [21769] 0.229 0.164 0.179 0.164 0.164 0.179 0.164 0.164 0.164 0.164 0.174 0.164
## [21781] 0.164 0.171 0.164 0.164 0.325 0.164 0.164 0.165 0.165 0.163 0.163 0.163
## [21793] 0.165 0.163 0.163 0.163 0.163 0.163 0.163 0.163 0.163 0.163 0.163 0.164
## [21805] 0.163 0.163 0.165 0.168 0.165 0.163 0.165 0.165 0.163 0.164 0.163 0.162
## [21817] 0.165 0.166 0.166 0.166 0.166 0.166 0.167 0.169 0.166 0.168 0.166 0.166
## [21829] 0.167 0.166 0.165 0.166 0.166 0.168 0.166 0.166 0.167 0.167 0.167 0.164
## [21841] 0.206 0.229 0.165 0.165 0.166 0.166 0.165 0.166 0.176 0.167 0.167 0.167
## [21853] 0.165 0.166 0.166 0.166 0.167 0.167 0.167 0.168 0.167 0.168 0.167 0.170
## [21865] 0.169 0.170 0.168 0.170 0.170 0.172 0.174 0.170 0.167 0.165 0.165 0.165
## [21877] 0.165 0.166 0.166 0.165 0.169 0.194 0.210 0.211 0.193 0.190 0.189 0.174
## [21889] 0.188 0.174 0.197 0.184 0.186 0.185 0.179 0.182 0.185 0.173 0.178 0.187
## [21901] 0.191 0.177 0.183 0.183 0.174 0.176 0.174 0.174 0.178 0.186 0.185 0.172
## [21913] 0.172 0.183 1.109 0.177 0.345 0.654 0.564 0.388 0.204 0.182 0.478 0.676
## [21925] 0.315 0.641 0.254 0.746 0.203 0.313 0.862 2.086 1.283 1.586 1.243 0.911
## [21937] 0.212 0.320 1.584 1.096 0.911 1.493 0.520 0.267 0.537 0.576 0.453 0.830
## [21949] 0.202 0.172 0.182 0.223 0.226 0.163 0.315 0.557 0.286 0.191 0.214 0.361
## [21961] 0.489 0.200 0.289 0.424 0.362 0.523 0.545 0.315 0.440 0.452 0.585 0.461
## [21973] 0.545 0.209 0.296 0.598 0.560 0.185 0.181 0.165 0.163 0.163 0.163 0.165
## [21985] 0.167 0.334 0.178 0.330 0.271 0.428 0.337 0.426 0.276 0.260 0.321 0.272
## [21997] 0.164 0.329 0.164 0.629 0.293 0.397 0.328 0.321 0.430 0.489 0.333 0.578
## [22009] 0.261 0.405 0.518 0.404 0.388 0.266 0.492 0.309 0.565 0.439 0.312 0.462
## [22021] 0.303 0.467 0.368 0.579 0.745 0.721 0.644 0.460 0.213 0.365 0.337 0.223
## [22033] 0.237 0.266 0.304 0.347 0.456 2.034 0.174 0.381 0.211 0.424 0.164 0.165
## [22045] 0.166 0.165 0.165 0.216 0.165 0.169 0.164 0.166 0.164 0.173 0.165 0.188
## [22057] 0.164 0.168 0.170 0.176 0.171 0.173 0.167 0.170 0.165 0.168 0.165 0.166
## [22069] 0.165 0.166 0.164 0.164 0.163 0.165 0.164 0.165 0.164 0.166 0.164 0.167
## [22081] 0.163 0.166 0.200 0.166 0.166 0.168 0.170 0.169 0.167 0.167 0.170 0.169
## [22093] 0.164 0.166 0.166 0.167 0.166 0.265 0.165 0.165 0.164 0.164 0.164 0.165
## [22105] 0.165 0.165 0.209 0.173 0.176 0.169 0.165 0.166 0.164 0.165 0.163 0.435
## [22117] 0.413 0.310 0.211 0.168 0.191 0.324 0.246 0.272 0.682 3.278 0.164 0.162
## [22129] 0.164 0.164 0.164 0.165 0.164 0.164 0.164 0.165 0.164 0.164 0.164 0.164
## [22141] 0.164 0.164 0.164 0.165 0.165 0.164 0.164 0.164 0.164 0.165 0.164 0.165
## [22153] 0.163 0.165 0.164 0.164 0.162 0.162 0.163 0.164 0.163 0.165 0.164 0.164
## [22165] 0.164 0.165 0.164 0.164 0.164 0.164 0.165 0.164 0.164 0.165 0.164 0.166
## [22177] 0.164 0.165 0.164 0.165 0.165 0.164 0.165 0.164 0.164 0.165 0.164 0.179
## [22189] 0.164 0.164 0.162 0.163 0.165 0.165 0.164 0.164 0.164 0.163 0.165 0.164
## [22201] 0.164 0.164 0.165 0.165 0.164 0.164 0.164 0.165 0.164 0.164 0.163 0.164
## [22213] 0.164 0.185 0.202 0.280 0.172 0.197 0.175 0.185 0.174 0.200 0.224 0.236
## [22225] 0.179 0.227 0.258 0.271 0.164 0.283 0.214 0.340 0.183 0.173 0.165 0.355
## [22237] 0.324 0.249 0.245 0.219 0.177 0.269 0.192 0.211 0.277 0.247 0.273 0.300
## [22249] 0.275 0.344 0.334 0.373 0.279 0.250 0.189 0.251 0.181 0.207 0.167 0.176
## [22261] 0.176 0.170 0.367 0.181 0.365 0.177 0.261 0.231 0.264 0.245 0.245 0.281
## [22273] 0.237 0.218 0.207 0.182 0.190 0.190 0.275 0.213 0.242 0.277 0.205 0.331
## [22285] 0.238 0.230 0.327 0.208 0.266 0.200 0.227 0.179 0.234 0.193 0.187 0.231
## [22297] 0.209 0.820 0.477 0.638 0.375 0.164 0.378 0.454 0.365 0.414 0.361 0.254
## [22309] 0.398 0.331 0.425 0.300 0.597 0.260 0.373 0.277 0.352 0.253 0.348 0.520
## [22321] 0.368 0.317 0.366 0.196 0.362 0.244 0.253 0.247 0.347 0.325 0.402 0.249
## [22333] 0.586 0.296 0.499 0.359 0.486 0.327 0.403 0.502 0.227 0.367 0.261 0.394
## [22345] 0.391 0.430 0.341 0.382 0.338 0.437 0.321 0.442 0.358 0.404 0.359 0.393
## [22357] 0.326 0.358 0.385 0.428 0.253 0.280 0.431 0.505 0.583 0.726 0.620 0.733
## [22369] 0.723 0.680 0.425 0.877 0.193 0.473 0.359 0.297 0.851 0.319 0.327 0.241
## [22381] 0.322 0.323 0.357 0.408 0.396 0.498 0.444 0.465 0.166 0.168 0.171 0.166
## [22393] 0.165 0.179 0.166 0.171 0.170 0.176 0.252 0.222 0.177 0.170 0.166 0.175
## [22405] 0.171 0.166 0.388 0.165 0.170 0.163 0.505 0.165 0.165 0.164 0.208 0.164
## [22417] 0.164 0.165 0.191 0.204 0.193 0.203 0.215 0.182 0.199 0.164 0.164 0.204
## [22429] 0.192 0.205 0.204 0.222 0.224 0.247 0.196 0.228 0.205 0.182 0.173 0.165
## [22441] 0.167 0.219 0.442 0.264 0.187 0.229 0.162 0.177 0.164 0.165 0.164 0.730
## [22453] 0.816 0.490 0.350 0.349 0.559 0.248 0.584 0.390 0.267 0.230 0.300 0.210
## [22465] 0.426 0.494 0.288 0.344 0.287 0.212 0.260 0.498 0.357 0.360 0.232 0.256
## [22477] 0.413 0.233 0.605 0.200 0.181 0.718 0.302 0.512 0.380 0.369 0.580 0.317
## [22489] 0.367 0.353 0.380 0.455 0.415 0.327 0.333 0.283 0.761 0.357 0.338 0.304
## [22501] 0.164 0.164 0.165 0.232 0.248 0.402 0.296 0.192 0.211 0.261 0.278 0.306
## [22513] 0.249 0.190 0.199 0.198 0.310 0.261 0.186 0.375 0.294 0.250 0.252 0.338
## [22525] 1.305 0.189 0.185 0.352 0.466 0.644 0.735 0.840 0.252 0.243 0.189 0.265
## [22537] 0.210 0.243 0.203 0.224 0.327 0.329 0.234 0.305 0.196 0.631 0.521 0.376
## [22549] 0.164 0.164 0.164 0.163 0.164 0.164 0.164 0.164 0.321 0.256 0.313 0.236
## [22561] 0.288 0.193 0.164 0.165 0.336 0.274 0.425 0.285 0.291 0.237 0.208 0.188
## [22573] 0.217 0.231 0.270 0.248 0.215 0.284 0.247 0.165 0.450 0.230 0.307 0.164
## [22585] 0.272 0.187 0.457 0.422 0.269 0.358 0.263 0.212 0.244 0.164 0.197 0.211
## [22597] 0.242 0.165 0.291 0.176 0.228 0.178 0.287 0.170 0.359 0.165 0.326 0.206
## [22609] 0.364 0.357 0.186 0.286 0.350 0.204 0.228 0.200 0.243 0.174 0.185 0.169
## [22621] 0.166 0.181 0.191 0.254 0.219 0.398 0.215 0.401 0.195 0.342 0.198 0.366
## [22633] 0.194 0.300 0.204 0.278 0.226 0.254 0.195 0.490 0.247 0.271 0.510 0.603
## [22645] 0.457 0.476 0.359 0.376 0.452 0.398 0.254 0.353 0.223 0.328 0.217 0.312
## [22657] 0.223 0.238 0.335 0.236 0.170 0.243 0.206 0.249 0.190 0.241 0.261 0.286
## [22669] 0.199 0.171 0.291 0.317 0.186 0.304 0.382 0.253 0.189 0.191 0.246 0.540
## [22681] 0.264 0.162 0.183 0.260 0.258 0.278 0.175 0.331 1.085 0.164 0.164 0.165
## [22693] 0.164 0.164 0.165 0.164 0.164 0.165 0.165 0.164 0.165 0.165 0.164 0.164
## [22705] 0.164 0.164 0.164 0.164 0.164 0.327 0.650 0.164 0.163 0.164 0.165 0.163
## [22717] 0.162 0.163 0.164 0.164 0.165 0.164 0.164 0.212 0.165 0.166 0.170 0.183
## [22729] 0.399 0.475 0.320 0.378 0.408 0.368 0.352 0.493 0.323 0.228 0.470 0.360
## [22741] 0.404 0.598 0.470 0.606 0.542 0.695 0.312 0.461 0.199 0.569 0.231 0.471
## [22753] 0.755 0.611 0.460 0.386 0.274 0.372 0.450 0.419 0.378 0.465 0.520 0.391
## [22765] 0.523 0.655 0.501 0.319 0.339 0.422 0.435 0.396 0.337 0.352 0.346 0.401
## [22777] 0.435 0.340 0.219 0.547 0.264 0.466 0.645 0.438 0.222 0.454 0.265 0.427
## [22789] 0.346 0.383 0.410 0.406 0.465 0.389 0.502 0.394 0.430 0.326 0.387 0.435
## [22801] 0.351 0.385 0.360 0.398 0.484 0.569 0.397 0.412 0.381 0.344 0.566 0.401
## [22813] 0.382 0.568 0.560 0.519 0.530 0.616 0.526 0.560 0.404 0.218 0.570 0.640
## [22825] 0.436 0.558 0.630 0.530 0.598 0.556 0.554 0.271 0.188 0.203 0.206 0.244
## [22837] 0.225 0.240 0.172 0.178 0.402 0.450 0.465 0.513 0.433 0.407 0.326 0.350
## [22849] 0.323 0.336 0.491 0.473 0.356 0.399 0.230 0.193 0.225 0.267 0.217 0.229
## [22861] 0.252 0.194 0.307 0.255 0.219 0.229 0.234 0.223 0.241 0.212 0.285 0.242
## [22873] 0.281 0.241 0.252 0.216 0.279 0.199 0.344 0.242 0.450 0.337 0.261 0.316
## [22885] 0.252 0.353 0.251 0.248 0.295 0.252 0.196 0.219 0.215 0.242 0.222 0.206
## [22897] 0.245 0.246 0.318 0.245 0.253 0.270 0.242 0.239 0.256 0.244 0.222 0.228
## [22909] 0.229 0.249 0.377 0.261 0.379 0.246 0.454 0.219 0.390 0.290 0.406 0.302
## [22921] 0.472 0.313 0.502 0.292 0.362 0.290 0.437 0.292 0.296 0.243 0.272 0.214
## [22933] 0.274 0.241 0.360 0.357 0.357 0.368 0.358 0.354 0.354 0.358 0.519 0.411
## [22945] 0.612 0.410 0.519 0.411 0.276 0.208 0.463 0.221 0.331 0.512 0.586 0.413
## [22957] 0.562 0.559 0.516 0.340 0.514 0.285 0.419 0.431 0.299 0.437 0.446 0.279
## [22969] 0.274 0.347 0.251 0.220 0.299 0.208 0.305 0.272 0.432 0.211 0.338 0.299
## [22981] 0.273 0.216 0.355 0.347 0.268 0.593 0.316 0.409 0.315 0.394 0.165 0.592
## [22993] 0.165 0.408 0.262 0.338 0.180 0.334 0.472 0.382 0.333 0.578 0.360 0.436
## [23005] 0.348 0.333 0.530 0.428 0.372 0.339 0.421 0.331 0.318 0.361 0.327 0.376
## [23017] 0.331 0.491 0.463 0.335 0.284 0.303 0.346 0.369 0.447 0.479 0.349 0.409
## [23029] 0.235 0.392 0.297 0.357 0.412 0.355 0.526 0.436 0.449 0.322 0.384 0.267
## [23041] 0.317 0.237 0.295 0.195 0.260 0.363 0.316 0.195 0.175 0.195 0.183 0.186
## [23053] 0.229 0.211 0.293 0.311 0.247 0.231 0.333 0.164 0.232 0.174 0.230 0.332
## [23065] 0.333 0.185 0.194 0.186 0.163 0.340 0.198 0.203 0.164 0.324 0.305 0.246
## [23077] 0.320 0.164 0.296 0.331 0.352 0.340 0.403 0.321 0.246 0.313 0.277 0.261
## [23089] 0.329 0.320 0.329 0.239 0.403 0.344 0.265 0.305 0.227 0.200 0.332 0.264
## [23101] 0.164 0.164 0.178 0.176 0.164 0.257 0.330 0.310 0.304 0.327 0.164 0.249
## [23113] 0.164 0.163 0.311 0.407 0.239 0.355 0.165 0.211 0.169 0.168 0.180 0.171
## [23125] 0.208 0.173 0.173 0.164 0.164 0.162 0.220 0.248 0.164 0.164 0.163 0.165
## [23137] 0.175 0.164 0.164 0.179 0.164 0.162 0.163 0.164 0.278 0.163 0.163 0.164
## [23149] 0.308 0.163 0.163 0.164 0.165 0.269 0.279 0.164 0.211 0.197 0.282 0.164
## [23161] 0.275 0.165 0.163 0.164 0.165 0.263 0.164 0.165 0.165 0.178 0.164 0.165
## [23173] 0.227 0.255 0.192 0.165 0.165 0.210 0.163 0.165 0.163 0.351 0.165 0.163
## [23185] 0.178 0.250 0.165 0.163 0.164 0.163 0.165 0.163 0.164 0.163 0.185 0.163
## [23197] 0.164 0.178 0.332 0.163 0.164 0.270 0.165 0.163 0.164 0.165 0.165 0.163
## [23209] 0.171 0.216 0.165 0.163 0.164 0.163 0.165 0.163 0.164 0.163 0.165 0.163
## [23221] 0.164 0.163 0.165 0.163 0.164 0.163 0.173 0.163 0.164 0.163 0.163 0.163
## [23233] 0.164 0.163 0.163 0.163 0.164 0.163 0.163 0.163 0.164 0.163 0.163 0.163
## [23245] 0.164 0.163 0.163 0.163 0.164 0.163 0.163 0.163 0.164 0.163 0.163 0.163
## [23257] 0.164 0.163 0.163 0.163 0.164 0.244 0.163 0.164 0.163 0.163 0.369 0.164
## [23269] 0.163 0.163 0.163 0.244 0.163 0.163 0.490 0.165 0.163 0.163 0.195 0.163
## [23281] 0.163 0.164 0.163 0.163 0.163 0.164 0.165 0.163 0.163 0.164 0.163 0.163
## [23293] 0.164 0.163 0.163 0.163 0.213 0.163 0.163 0.163 0.164 0.163 0.163 0.163
## [23305] 0.164 0.165 0.163 0.163 0.164 0.163 0.163 0.163 0.247 0.165 0.163 0.163
## [23317] 0.182 0.165 0.163 0.163 0.164 0.165 0.163 0.164 0.164 0.165 0.184 0.163
## [23329] 0.164 0.163 0.163 0.163 0.164 0.163 0.163 0.163 0.164 0.163 0.163 0.163
## [23341] 0.164 0.194 0.163 0.163 0.164 0.165 0.163 0.164 0.163 0.164 0.163 0.165
## [23353] 0.164 0.163 0.165 0.164 0.163 0.165 0.164 0.165 0.164 0.165 0.164 0.164
## [23365] 0.164 0.164 0.164 0.163 0.164 0.164 0.163 0.164 0.164 0.164 0.163 0.164
## [23377] 0.164 0.164 0.164 0.164 0.163 0.180 0.165 0.163 0.222 0.163 0.182 0.178
## [23389] 0.272 0.163 0.163 0.392 0.163 0.163 0.163 0.163 0.169 0.163 0.163 0.163
## [23401] 0.163 0.164 0.163 0.163 0.163 0.163 0.178 0.164 0.164 0.214 0.163 0.163
## [23413] 0.313 0.323 0.415 0.331 0.312 0.184 0.238 0.164 0.163 0.163 0.163 0.163
## [23425] 0.330 0.186 0.453 0.163 0.163 0.163 0.163 0.164 0.209 0.199 0.163 0.436
## [23437] 0.331 0.164 0.213 0.333 0.490 0.182 0.206 0.211 0.207 0.175 0.164 0.163
## [23449] 0.210 0.164 0.402 0.334 0.323 0.181 0.200 0.163 0.163 0.184 0.248 0.164
## [23461] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.171 0.241 0.164 0.164
## [23473] 0.329 0.231 0.164 0.164 0.181 0.251 0.165 0.164 0.310 0.269 0.164 0.193
## [23485] 0.332 0.164 0.165 0.321 0.184 0.165 0.165 0.317 0.195 0.165 0.165 0.241
## [23497] 0.320 0.165 0.164 0.295 0.322 0.165 0.165 0.177 0.174 0.165 0.165 0.171
## [23509] 0.163 0.165 0.165 0.164 0.169 0.164 0.165 0.167 0.164 0.164 0.165 0.206
## [23521] 0.169 0.164 0.164 0.178 0.296 0.164 0.164 0.164 0.319 0.164 0.165 0.164
## [23533] 0.323 0.165 0.165 0.178 0.332 0.164 0.165 0.191 0.226 0.164 0.165 0.320
## [23545] 0.332 0.164 0.165 0.307 0.293 0.164 0.165 0.332 0.254 0.164 0.238 0.177
## [23557] 0.164 0.164 0.305 0.254 0.164 0.164 0.313 0.168 0.164 0.164 0.199 0.179
## [23569] 0.164 0.164 0.261 0.177 0.164 0.164 0.258 0.267 0.164 0.164 0.392 0.330
## [23581] 0.164 0.164 0.164 0.284 0.164 0.164 0.331 0.315 0.164 0.164 0.333 0.200
## [23593] 0.164 0.164 0.201 0.323 0.164 0.164 0.202 0.205 0.164 0.164 0.251 0.164
## [23605] 0.164 0.316 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.246 0.164 0.164
## [23617] 0.164 0.164 0.164 0.303 0.164 0.164 0.164 0.279 0.164 0.330 0.163 0.164
## [23629] 0.164 0.183 0.181 0.164 0.164 0.276 0.164 0.164 0.178 0.164 0.280 0.169
## [23641] 0.164 0.164 0.169 0.164 0.162 0.164 0.164 0.211 0.164 0.164 0.186 0.167
## [23653] 0.206 0.169 0.164 0.164 0.179 0.169 0.164 0.164 0.183 0.222 0.165 0.164
## [23665] 0.164 0.164 0.165 0.164 0.163 0.164 0.163 0.164 0.165 0.163 0.165 0.165
## [23677] 0.178 0.165 0.165 0.163 0.165 0.164 0.163 0.165 0.163 0.165 0.165 0.165
## [23689] 0.165 0.164 0.165 0.164 0.164 0.164 0.167 0.164 0.165 0.173 0.163 0.165
## [23701] 0.164 0.179 0.165 0.164 0.164 0.164 0.165 0.186 0.164 0.165 0.164 0.163
## [23713] 0.172 0.165 0.164 0.164 0.164 0.163 0.164 0.164 0.313 0.164 0.164 0.209
## [23725] 0.163 0.164 0.164 0.164 0.163 0.164 0.164 0.164 0.163 0.164 0.251 0.216
## [23737] 0.164 0.163 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.333 0.164
## [23749] 0.164 0.164 0.164 0.164 0.164 0.246 0.164 0.164 0.351 0.164 0.164 0.331
## [23761] 0.332 0.164 0.164 0.164 0.164 0.164 0.164 0.313 0.238 0.164 0.164 0.330
## [23773] 0.199 0.317 0.164 0.165 0.164 0.164 0.164 0.164 0.164 0.192 0.164 0.164
## [23785] 0.170 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.170 0.164 0.164
## [23797] 0.164 0.164 0.164 0.164 0.164 0.308 0.164 0.309 0.222 0.199 0.197 0.164
## [23809] 0.309 0.278 0.172 0.164 0.164 0.165 0.337 0.164 0.164 0.165 0.165 0.220
## [23821] 0.164 0.165 0.164 0.165 0.184 0.236 0.164 0.164 0.165 0.194 0.199 0.164
## [23833] 0.163 0.163 0.163 0.165 0.163 0.163 0.163 0.165 0.165 0.165 0.163 0.163
## [23845] 0.165 0.164 0.163 0.165 0.163 0.163 0.165 0.165 0.163 0.165 0.163 0.163
## [23857] 0.165 0.165 0.163 0.165 0.163 0.187 0.165 0.163 0.165 0.163 0.181 0.164
## [23869] 0.163 0.165 0.217 0.164 0.164 0.165 0.163 0.163 0.164 0.164 0.222 0.164
## [23881] 0.403 0.164 0.164 0.165 0.164 0.164 0.164 0.165 0.204 0.232 0.211 0.165
## [23893] 0.239 0.175 0.164 0.164 0.165 0.164 0.164 0.164 0.207 0.331 0.164 0.165
## [23905] 0.165 0.163 0.171 0.224 0.165 0.163 0.163 0.324 0.163 0.163 0.163 0.216
## [23917] 0.163 0.163 0.163 0.163 0.258 0.163 0.163 0.163 0.245 0.250 0.163 0.163
## [23929] 0.165 0.163 0.163 0.163 0.163 0.163 0.163 0.163 0.163 0.165 0.163 0.181
## [23941] 0.163 0.163 0.165 0.248 0.181 0.163 0.165 0.163 0.301 0.163 0.163 0.163
## [23953] 0.316 0.163 0.163 0.163 0.163 0.163 0.163 0.163 0.163 0.204 0.341 0.163
## [23965] 0.163 0.239 0.164 0.163 0.163 0.163 0.385 0.163 0.163 0.165 0.331 0.163
## [23977] 0.163 0.163 0.163 0.163 0.165 0.163 0.164 0.204 0.163 0.165 0.163 0.345
## [23989] 0.164 0.163 0.163 0.165 0.163 0.181 0.205 0.163 0.163 0.165 0.164 0.236
## [24001] 0.163 0.163 0.165 0.165 0.164 0.333 0.163 0.163 0.163 0.165 0.163 0.280
## [24013] 0.165 0.163 0.165 0.163 0.203 0.163 0.166 0.163 0.163 0.303 0.165 0.163
## [24025] 0.165 0.163 0.252 0.165 0.163 0.163 0.250 0.165 0.163 0.165 0.165 0.164
## [24037] 0.164 0.165 0.163 0.165 0.163 0.164 0.164 0.198 0.165 0.163 0.164 0.164
## [24049] 0.165 0.165 0.163 0.164 0.244 0.163 0.164 0.164 0.165 0.163 0.164 0.165
## [24061] 0.165 0.164 0.189 0.183 0.164 0.332 0.165 0.276 0.164 0.174 0.165 0.163
## [24073] 0.165 0.165 0.164 0.309 0.165 0.165 0.172 0.165 0.164 0.322 0.165 0.164
## [24085] 0.163 0.165 0.164 0.163 0.165 0.165 0.164 0.163 0.164 0.165 0.164 0.176
## [24097] 0.164 0.165 0.164 0.163 0.164 0.164 0.164 0.164 0.164 0.164 0.165 0.165
## [24109] 0.165 0.168 0.165 0.164 0.174 0.164 0.165 0.164 0.165 0.336 0.164 0.165
## [24121] 0.164 0.171 0.164 0.163 0.164 0.164 0.164 0.164 0.164 0.164 0.179 0.164
## [24133] 0.242 0.164 0.167 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [24145] 0.301 0.164 0.164 0.177 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [24157] 0.164 0.164 0.164 0.164 0.180 0.164 0.164 0.168 0.164 0.199 0.163 0.193
## [24169] 0.175 0.312 0.164 0.164 0.248 0.182 0.237 0.195 0.179 0.164 0.165 0.164
## [24181] 0.165 0.164 0.164 0.165 0.164 0.165 0.165 0.165 0.164 0.165 0.165 0.163
## [24193] 0.165 0.164 0.165 0.165 0.164 0.164 0.164 0.164 0.165 0.164 0.164 0.164
## [24205] 0.164 0.165 0.165 0.165 0.163 0.164 0.165 0.165 0.163 0.164 0.165 0.165
## [24217] 0.164 0.165 0.186 0.163 0.164 0.164 0.252 0.164 0.164 0.222 0.164 0.163
## [24229] 0.164 0.163 0.164 0.164 0.163 0.164 0.163 0.164 0.163 0.164 0.164 0.163
## [24241] 0.164 0.163 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164
## [24253] 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.164 0.382 0.332 0.164 0.181
## [24265] 0.164 0.164 0.323 0.164 0.163 0.164 0.164 0.237 0.164 0.164 0.164 0.254
## [24277] 0.164 0.351 0.165 0.164 0.165 0.165 0.165 0.165 0.165 0.165 0.185 0.165
## [24289] 0.164 0.164 0.165 0.331 0.165 0.165 0.165 0.163 0.165 0.165 0.165 0.163
## [24301] 0.165 0.164 0.165 0.164 0.165 0.164 0.164 0.164 0.164 0.164 0.164 0.165
## [24313] 0.164 0.165 0.164 0.164 0.165 0.327 0.164 0.165 0.163 0.164 0.165 0.164
## [24325] 0.163 0.164 0.164 0.164 0.164 0.163 0.164 0.164 0.164 0.164 0.164 0.164
## [24337] 0.164 0.164 0.164 0.164 0.163 0.163 0.164 0.290 0.164 0.333 0.289 0.270
## [24349] 0.192 0.290 0.681 0.293 0.672 0.304 0.698 0.599 0.419 0.449 0.261 0.221
## [24361] 0.182 0.180 0.192 0.240 0.404 0.545 0.364 0.288 0.318 0.173 0.275 0.239
## [24373] 0.195 0.254 0.562 0.331 0.302 0.314 0.311 0.281 0.615 0.190 0.487 0.222
## [24385] 0.568 0.662 0.703 0.164 0.572 0.165 0.516 0.164 0.652 0.873 0.166 0.224
## [24397] 0.177 0.475 0.399 0.690 1.443 0.564 0.509 0.419 0.481 0.612 0.970 0.435
## [24409] 0.599 0.671 0.916 0.675 0.390 0.453 0.792 1.186 0.935 0.726 0.851 0.435
## [24421] 0.629 0.718 1.008 0.454 0.597 0.432 0.373 0.444 0.544 0.653 0.377 0.702
## [24433] 0.398 0.469 0.391 0.331 0.442 0.365 0.362 0.265 0.601 0.522 0.605 0.541
## [24445] 0.534 0.636 0.449 0.439 0.486 0.431 0.454 0.412 0.396 0.581 0.302 0.222
## [24457] 0.300 0.732 0.265 0.614 0.316 0.475 0.251 0.321 0.406 0.591 0.571 0.210
## [24469] 0.381 0.289 0.269 0.291 0.490 0.259 0.274 0.411 0.586 0.382 0.245 0.242
## [24481] 0.364 0.664 0.237 1.527 1.016 0.919 0.768 0.758 0.820 1.663 0.332 0.569
## [24493] 0.666 0.421 1.079 0.777 1.020 1.115 0.298 0.231 0.178 0.258 0.361 0.347
## [24505] 0.565 0.216 0.433 0.297 0.363 0.296 0.294 0.298 0.381 0.327 0.443 0.373
## [24517] 0.560 0.332 0.416 0.424 0.345 0.429 0.276 0.340 0.593 0.374 0.606 0.336
## [24529] 0.488 0.371 0.577 0.258 0.411 0.239 0.272 0.229 0.246 0.261 0.284 0.280
## [24541] 0.179 0.164 0.163 0.164 0.165 0.225 0.185 0.206 0.826 0.951 0.283 0.759
## [24553] 0.340 0.450 0.668 0.239 0.263 0.461 0.330 0.620 0.584 0.300 1.047 1.089
## [24565] 0.895 0.372 0.797 0.392 1.930 0.472 0.203 0.285 0.225 0.221 0.219 0.290
## [24577] 0.502 0.284 0.247 0.259 0.496 0.473 0.254 0.284 0.329 0.338 0.319 0.180
## [24589] 0.164 0.163 0.164 0.165 0.164 0.163 0.443 0.956 0.199 0.304 1.067 0.772
## [24601] 0.890 0.872 1.188 0.307 0.260 0.431 0.300 0.395 0.306 0.538 0.413 0.429
## [24613] 0.371 0.532 0.450 0.511 0.409 0.310 0.528 0.163 0.164 0.162 0.164 0.165
## [24625] 0.164 0.164 0.164 0.164 0.164 0.371 0.163 0.165 0.164 0.163 0.162 0.325
## [24637] 0.280 0.350 0.297 0.284 0.206 0.366 0.424 0.486 0.437 0.386 0.500 0.528
## [24649] 0.588 0.199 0.197 0.331 0.297 0.316 0.421 0.299 1.350 0.224 0.635 0.197
## [24661] 0.269 0.233 1.623 0.291 0.815 0.565 0.395 0.929 0.270 0.238 0.742 0.268
## [24673] 0.214 0.197 0.203 0.233 0.218 0.215 0.458 0.228 0.269 0.341 0.456 0.351
## [24685] 0.227 0.191 0.382 0.205 0.308 0.313 0.239 0.225 0.220 0.168 0.184 0.358
## [24697] 1.794 0.455 0.544 0.365 1.586 0.350 0.453 0.728 1.075 0.937 0.786 0.252
## [24709] 0.273 0.506 0.659 0.306 0.187 0.164 0.163 0.164 0.165 0.171 0.163 0.173
## [24721] 0.181 0.340 0.763 0.272 0.393 0.199 0.223 0.372 0.388 0.448 0.482 0.507
## [24733] 0.543 0.535 0.441 0.592 0.671 0.436 0.525 0.401 0.404 0.376 0.300 0.374
## [24745] 0.368 0.449 0.432 0.340 0.394 0.405 0.360 0.519 0.452 0.516 0.407 0.551
## [24757] 0.373 0.406 0.337 0.282 0.350 0.417 0.326 0.282 0.260 0.409 0.332 0.206
## [24769] 0.205 0.447 0.239 0.612 0.284 0.251 0.355 0.185 0.164 0.163 0.164 0.165
## [24781] 0.164 0.163 0.166 0.179 0.794 0.226 0.489 0.916 0.633 0.551 0.590 0.609
## [24793] 0.658 0.635 0.701 0.470 0.379 0.409 0.680 0.956 0.296 0.269 0.272 0.287
## [24805] 0.347 0.252 0.307 0.259 0.362 0.616 0.396 0.358 0.372 0.293 0.386 0.463
## [24817] 0.463 0.497 0.315 0.400 0.383 0.354 0.351 0.308 0.395 0.342 0.412 0.273
## [24829] 0.493 0.400 0.631 0.203 0.310 0.260 0.358 0.380 0.256 1.395 0.820 0.537
## [24841] 0.818 0.924 0.920 0.340 0.481 0.584 0.359 0.462 0.438 0.995 1.124 0.430
## [24853] 0.656 0.223 0.472 0.270 0.395 0.260 0.266 0.386 0.266 0.294 0.321 0.243
## [24865] 0.293 0.852 0.341 0.261 0.385 0.297 0.369 0.265 0.228 0.397 0.350 0.413
## [24877] 0.413 0.259 0.376 0.198 0.198 0.168 0.165 0.164 0.164 0.165 0.164 0.164
## [24889] 0.164 0.214 0.164 0.480 0.495 0.457 0.507 0.514 0.486 0.314 0.197 0.163
## [24901] 0.165 0.162 0.170 0.172 0.165 0.165 0.166 0.165 0.178 0.176 0.181 0.288
## [24913] 0.230 0.266 0.346 0.425 0.227 0.298 0.274 0.327 0.241 0.457 0.548 0.275
## [24925] 0.587 0.334 0.508 0.438 0.591 0.200 0.479 0.460 0.408 0.364 0.365 0.517
## [24937] 0.433 0.290 0.524 0.192 0.164 0.163 0.164 0.165 0.162 0.163 0.162 0.162
## [24949] 0.164 0.164 0.164 0.164 0.164 0.170 0.168 0.165 0.164 0.173 0.194 0.212
## [24961] 0.303 0.411 0.277 0.359 0.235 0.304 0.195 0.257 0.201 0.234 0.204 0.258
## [24973] 0.246 0.239 0.281 0.255 0.293 0.276 0.407 0.333 0.236 0.241 0.287 0.274
## [24985] 0.360 0.349 0.165 0.165 0.164 0.164 0.164 0.163 0.164 0.162 0.162 0.168
## [24997] 0.178 0.219 0.216 0.204 0.371 0.316 0.459 0.447 0.466 0.226 0.547 0.424
## [25009] 0.441 0.554 0.361 0.165 0.162 0.163 0.175 0.166 0.192 0.166 0.170 0.179
## [25021] 0.174 0.165 0.165 0.171 0.181 0.162 0.164 0.163 0.164 0.165 0.166 0.162
## [25033] 0.163 0.164 0.175 0.171 0.164 0.165 0.164 0.162 0.176 0.254 0.167 0.176
## [25045] 0.240 0.162 0.276 0.162 0.181 0.162 0.177 0.162 0.229 0.162 0.182 0.165
## [25057] 0.162 0.195 0.162 0.208 0.162 0.166 0.176 0.164 0.168 0.197 0.174 0.164
## [25069] 0.163 0.164 0.163 0.165 0.163 0.165 0.163 0.165 0.163 0.165 0.163 0.165
## [25081] 0.163 0.165 0.163 0.165 0.163 0.165 0.163 0.164 0.165 0.164 0.164 0.164
## [25093] 0.167 0.163 0.165 0.162 0.164 0.164 0.164 0.164 0.164 0.164 0.163 0.164
## [25105] 0.162 0.162 0.162 0.170 0.217 0.279 0.226 0.164 0.164 0.165 0.165 0.164
## [25117] 0.164 0.165 0.164 0.163 0.165 0.163 0.164 0.163 0.165 0.164 0.163 0.165
## [25129] 0.162 0.164 0.163 0.237 0.309 0.217 0.222 0.319 0.251 0.333 0.237 0.256
## [25141] 0.361 0.440 0.213 0.389 0.320 0.250 0.363 0.432 0.256 0.211 0.369 0.424
## [25153] 0.305 0.285 0.319 0.355 0.379 0.256 0.231 0.216 0.258 0.393 0.363 0.401
## [25165] 0.383 0.406 0.382 0.373 0.337 0.169 0.244 0.178 0.172 0.180 0.227 0.203
## [25177] 0.202 0.201 0.201 0.210 0.249 0.189 0.194 0.224 0.237 0.259 0.212 0.163
## [25189] 0.164 0.163 0.190 0.166 0.196 0.167 0.200 0.166 0.200 0.166 0.204 0.165
## [25201] 0.192 0.166 0.173 0.164 0.164 0.165 0.167 0.165 0.172 0.208 0.175 0.166
## [25213] 0.174 0.175 0.178 0.188 0.197 0.191 0.198 0.198 0.175 0.181 0.196 0.166
## [25225] 0.248 0.285 0.226 0.177 0.180 0.195 0.332 0.299 0.241 0.278 0.287 0.273
## [25237] 0.362 0.335 0.267 0.191 0.328 0.211 0.349 0.347 0.475 0.177 0.206 0.264
## [25249] 0.261 0.246 0.370 0.257 0.365 0.361 0.403 0.306 0.329 0.405 0.413 0.386
## [25261] 0.369 0.352 0.285 0.374 0.366 0.382 0.199 0.226 0.189 0.196 0.219 0.212
## [25273] 0.217 0.210 0.197 0.226 0.213 0.221 0.204 0.229 0.240 0.208 0.191 0.213
## [25285] 0.256 0.441 0.231 0.210 0.254 0.400 0.355 0.195 0.265 0.207 0.189 0.202
## [25297] 0.220 0.208 0.200 0.193 0.196 0.181 0.178 0.178 0.185 0.212 0.177 0.175
## [25309] 0.200 0.190 0.210 0.206 0.193 0.178 0.203 0.190 0.177 0.200 0.196 0.238
## [25321] 0.164 0.228 0.180 0.203 0.191 0.193 0.205 0.207 0.182 0.209 0.195 0.217
## [25333] 0.204 0.198 0.218 0.211 0.189 0.171 0.341 0.342 0.316 0.208 0.179 0.196
## [25345] 0.187 0.256 0.246 0.257 0.316 0.256 0.243 0.317 0.314 0.325 0.229 0.165
## [25357] 0.306 0.164 0.306 0.164 0.322 0.166 0.164 0.166 0.164 0.167 0.164 0.164
## [25369] 0.165 0.166 0.164 0.164 0.164 0.166 0.164 0.164 0.164 0.164 0.164 0.164
## [25381] 0.169 0.164 0.164 0.250 0.164 0.188 0.165 0.164 0.164 0.197 0.164 0.164
## [25393] 0.164 0.164 0.179 0.175 0.167 0.175 0.166 0.185 0.173 0.192 0.211 0.205
## [25405] 0.232 0.526 0.192 0.201 0.218 0.202 0.191 0.181 0.164 0.167 0.168 0.165
## [25417] 0.168 0.169 0.168 0.173 0.168 0.183 0.166 0.187 0.169 0.187 0.169 0.190
## [25429] 0.188 0.180 0.205 0.213 0.203 0.197 0.175 0.192 0.205 0.177 0.189 0.191
## [25441] 0.181 0.187 0.183 0.170 0.172 0.173 0.175 0.182 0.193 0.193 0.171 0.165
## [25453] 0.180 0.163 0.201 0.193 0.193 0.165 0.185 0.179 0.221 0.213 0.180 0.218
## [25465] 0.202 0.193 0.189 0.164 0.164 0.164 0.164 0.316 0.354 0.195 0.278 0.244
## [25477] 0.390 0.245 0.207 0.193 0.224 0.466 0.441 0.193 0.214 0.228 0.220 0.248
## [25489] 0.305 0.170 0.165 0.163 0.199 0.168 0.168 0.168 0.167 0.166 0.232 0.166
## [25501] 0.166 0.164 0.169 0.165 0.167 0.164 0.166 0.165 0.164 0.165 0.171 0.311
## [25513] 0.164 0.168 0.165 0.167 0.173 0.190 0.174 0.163 0.171 0.211 0.164 0.164
## [25525] 0.265 0.364 0.165 0.165 0.163 0.267 0.165 0.162 0.163 0.162 0.162 0.164
## [25537] 0.165 0.162 0.164 0.163 0.164 0.164 0.162 0.165 0.162 0.164 0.163 0.181
## [25549] 0.167 0.262 0.228 0.297 0.305 0.491 0.246 0.188 0.163 0.163 0.162 0.162
## [25561] 0.164 0.162 0.163 0.164 0.163 0.162 0.165 0.192 0.177 0.232 0.164 0.164
## [25573] 0.340 0.197 0.167 0.164 0.165 0.169 0.169 0.171 0.182 0.180 0.178 0.175
## [25585] 0.181 0.178 0.181 0.186 0.205 0.232 0.192 0.190 0.186 0.165 0.279 0.165
## [25597] 0.165 0.164 0.165 0.163 0.328 0.187 0.182 0.175 0.183 0.172 0.169 0.167
## [25609] 0.168 0.186 0.170 0.170
getFitted(Final8.1)  #predictions of the model for all points
x1 = getSimulations(Final8.1, nsim = 5, type = "refit")  #extract simulations from the model
getRefit(Final8.1, x1[[1]])  #model with simulated data
## Formula:          
## KUD50 ~ LengthStd + Habitat + MonitArea_km2 + (1 | Transmitter) +  
##     (1 | Species)
## Data: newData
##       AIC       BIC    logLik  df.resid 
## -75572.88 -75507.68  37794.44     25604 
## Random-effects (co)variances:
## 
## Conditional model:
##  Groups      Name        Std.Dev.
##  Transmitter (Intercept) 0.2655  
##  Species     (Intercept) 0.1463  
## 
## Number of obs: 25612 / Conditional model: Transmitter, 850; Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.062 
## 
## Fixed Effects:
## 
## Conditional model:
##            (Intercept)               LengthStd         Habitatdemersal  
##               -1.84310                 0.41262                -0.06733  
## Habitatpelagic-neritic           MonitArea_km2  
##                0.29670                 0.01764
getRefit(Final8.1, getObservedResponse(Final8.1))  #model with real data
## Formula:          
## KUD50 ~ LengthStd + Habitat + MonitArea_km2 + (1 | Transmitter) +  
##     (1 | Species)
## Data: newData
##       AIC       BIC    logLik  df.resid 
## -76253.57 -76188.36  38134.78     25604 
## Random-effects (co)variances:
## 
## Conditional model:
##  Groups      Name        Std.Dev.
##  Transmitter (Intercept) 0.2690  
##  Species     (Intercept) 0.1575  
## 
## Number of obs: 25612 / Conditional model: Transmitter, 850; Species, 30
## 
## Dispersion estimate for Gamma family (sigma^2): 0.0607 
## 
## Fixed Effects:
## 
## Conditional model:
##            (Intercept)               LengthStd         Habitatdemersal  
##               -1.75314                 0.21974                -0.12544  
## Habitatpelagic-neritic           MonitArea_km2  
##                0.36534                 0.02219
#create a dataframe with the simulated data and the true data
df1 <- data.frame(x1$sim_1, x1$sim_2, x1$sim_3, week_kuds$KUD50, week_kuds$LengthStd, week_kuds$Habitat)

#plot KUD50 (real and simulated) against LengthStd
grid.arrange(ggplot(data= df1, aes(x = week_kuds.LengthStd, y=week_kuds.KUD50)) + geom_point(col="gray") +  scale_y_continuous(limits = c(0, 4)) + xlab("Length Std") + ylab("real KUD50"), ggplot(data= df1, aes(x = week_kuds.LengthStd, y=x1.sim_1)) + geom_point(col="green3") +  scale_y_continuous(limits = c(0, 4)) + xlab("Length Std") + ylab("KUD50 simulation 1"), ggplot(data= df1, aes(x = week_kuds.LengthStd, y=x1.sim_2)) + geom_point(col="green") +  scale_y_continuous(limits = c(0, 4)) + xlab("Length Std") + ylab("KUD50 simulation 2"), ggplot(data= df1, aes(x = week_kuds.LengthStd, y=x1.sim_3)) + geom_point(col="green4") +  scale_y_continuous(limits = c(0, 4)) + xlab("Length Std") + ylab("KUD50 simulation 3"))

#plot KUD50 (real and simulated) against Habitat
grid.arrange(ggplot(data = df1, aes(x = week_kuds.Habitat, y=week_kuds.KUD50)) +
  geom_boxplot(fill = "gray") + scale_y_continuous(limits = c(0, 4)) + xlab("Habitat") + ylab("real KUD50"), ggplot(data = df1, aes(x = week_kuds.Habitat, y=x1.sim_1)) +
  geom_boxplot(fill = "green3") + scale_y_continuous(limits = c(0, 4)) + xlab("Habitat") + ylab("KUD50 simulation 1"), ggplot(data = df1, aes(x = week_kuds.Habitat, y=x1.sim_2)) +
  geom_boxplot(fill = "green") + scale_y_continuous(limits = c(0, 4)) + xlab("Habitat") + ylab("KUD50 simulation 2"), ggplot(data = df1, aes(x = week_kuds.Habitat, y=x1.sim_3)) + geom_boxplot(fill = "green4") + scale_y_continuous(limits = c(0, 4)) + xlab("Habitat") + ylab("KUD50 simulation 3"), ncol = 4)

The normality and homoscedasticity assumptions aren’t met. However, given the large dataset, this violation may not be a big problem. To investigate this, we simulated the response values and compared with the real ones. After observing the results, we concluded that the patterns were similar and the model can be said to be well adjusted.